Deep Learning Specialization - Andrew

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Last Update	05/18/2023
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Intermediate

01. Neural Network and Deep Learning
- deep-learning-notation.pdf
  
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Week1
- 01-welcome.mp4
  
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- 02-what-is-neural-network.mp4
  
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- 03-supervised-learning-with-neural-network.mp4
  
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- 04-why-is-deeplearning-taking-off.mp4
  
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- 05-about-this-course.mp4
  
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- 06-course-resourse.mp4
  
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- 08-hero-interview.mp4
  
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- Frequently Asked Questions _ Coursera.pdf
  
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- How to use Discussion Forums _ Coursera.pdf
  
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Quiz
- Introduction to deep learning _ Coursera.mhtml
  
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Week2
- 01-Binary Classification.mp4
  
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- 02-Logistic Regression.mp4
  
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- 03-Logistic Regression Cost Function.mp4
  
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- 04-Gradient Descent.mp4
  
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- 05-Derivatives.mp4
  
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- 06-More Derivative Examples.mp4
  
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- 07-Computation graph.mp4
  
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- 08-Derivatives with a Computation Graph.mp4
  
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- 09-Logistic Regression Gradient Descent.mp4
  
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- 10-Gradient Descent on m Examples.mp4
  
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- 11-Vectorization.mp4
  
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- 12-More Vectorization Examples .mp4
  
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- 13-Vectorizing Logistic Regression.mp4
  
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- 14-Vectorizing Logistic Regression_s Gradient Output.mp4
  
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- 15-Broadcasting in Python .mp4
  
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- 16-A note on python numpy vectors.mp4
  
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- 17-Quick tour of Jupyter-iPython Notebooks.mp4
  
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- 18-Explanation of logistic regression cost function (optional).mp4
  
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- 19-Pieter Abbeel interview.mp4
  
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Assignment
- Deep Learning Honor Code _ Coursera.pdf
  
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images
- image2vector_kiank.png
  
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- image2vector.png
  
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- Sigmoid.png
  
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- Python Basics With Numpy v3.ipynb
  
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- Python_Basics_With_Numpy_v3a.ipynb
  
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datasets
- test_catvnoncat.h5
  
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- train_catvnoncat.h5
  
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images
- cat_in_iran.jpg
  
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- gargouille.jpg
  
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- image1.png
  
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- image2.png
  
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- la_defense.jpg
  
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- LogReg_kiank.png
  
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- my_image.jpg
  
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- my_image2.jpg
  
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- Logistic Regression with a Neural Network mindset v4 - Stanford.ipynb
  
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- Logistic Regression with a Neural Network mindset v4.ipynb
  
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- Logistic Regression with a Neural Network mindset v5.ipynb
  
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- Logistic_Regression_with_a_Neural_Network_mindset_v6a.ipynb
  
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- lr_utils.py
  
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- Programming Assignment FAQ _ Coursera.pdf
  
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- Clarification about Upcoming Gradient Descent Video _ Coursera.pdf
  
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- Clarification about Upcoming Logistic Regression Cost Function Video _ Coursera.pdf
  
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- Clarification of _dz_ _ Coursera.pdf
  
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- Copy of Clarification about Upcoming Logistic Regression Cost Function Video _ Coursera.pdf
  
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- Derivation of DL_dz (optional reading) _ Coursera.pdf
  
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Week3
- 01-Neural Networks Overview.mp4
  
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- 02-Neural Network Representation .mp4
  
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- 03-Computing a Neural Network_s Output.mp4
  
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- 04-Vectorizing across multiple examples.mp4
  
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- 05-Explanation for Vectorized Implementation.mp4
  
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- 06-Activation functions.mp4
  
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- 07-Why do you need non-linear activation functions.mp4
  
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- 08-Derivatives of activation functions.mp4
  
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- 09-Gradient descent for Neural Networks.mp4
  
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- 10-Backpropagation intuition (optional).mp4
  
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- 11-Random Initialization.mp4
  
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- 12-Ian Goodfellow interview.mp4
  
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images
- classification_kiank.png
  
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- grad_summary.png
  
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- sgd_bad.gif
  
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- sgd.gif
  
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- Planar data classification with one hidden layer v4.ipynb
  
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- Planar data classification with one hidden layer v5.ipynb
  
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- Planar_data_classification_with_onehidden_layer_v6a.ipynb
  
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- Planar_data_classification_with_onehidden_layer_v6b.ipynb
  
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- Planar_data_classification_with_onehidden_layer_v6c.ipynb
  
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- planar_utils.py
  
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- testCases_v2.py
  
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- Clarification about Upcoming Backpropagation intuition (optional) _ Coursera.pdf
  
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- Clarification_ Activation Function _ Coursera.pdf
  
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- FlowerModel.png
  
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Week4
- 01-Deep L-layer neural network.mp4
  
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- 02-Forward Propagation in a Deep Network.mp4
  
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- 03-Getting your matrix dimensions right .mp4
  
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- 04-Why deep representations.mp4
  
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- 05-Building blocks of deep neural networks .mp4
  
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- 06-Forward and Backward Propagation.mp4
  
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- 07-Parameters vs Hyperparameters.mp4
  
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- 08-What does this have to do with the brain.mp4
  
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images
- 2layerNN.png
  
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- backpass.png
  
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- backprop_kiank.png
  
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- backprop.png
  
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- final outline.png
  
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- imvector.png
  
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- linearback_kiank.png
  
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- mn_backward.png
  
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- model_architecture_kiank.png
  
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- model_architecture2.png
  
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- n_model_backward.png
  
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- NlayerNN.png
  
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- nm_backward.png
  
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- relu.png
  
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- structure.png
  
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- testCases_v3.py
  
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- testCases_v4.py
  
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- testCases_v4a.py
  
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datasets
- test_catvnoncat.h5
  
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- train_catvnoncat.h5
  
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- Deep Neural Network - Application v3.ipynb
  
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- Deep Neural Network - Application v4.ipynb
  
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- Deep Neural Network - Application v5.ipynb
  
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- Deep Neural Network - Application v6.ipynb
  
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- Deep Neural Network - Application v7.ipynb
  
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- Deep Neural Network - Application v8.ipynb
  
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- dnn_app_utils_v2.py
  
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- dnn_app_utils_v3.py
  
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images
- 2layerNN_kiank.png
  
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- imvector.png
  
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- imvectorkiank.png
  
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- LlayerNN_kiank.png
  
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- my_image.jpg
  
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- Clarification about Getting your matrix dimensions right video _ Coursera.pdf
  
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- Clarification about Upcoming Forward and Backward Propagation Video _ Coursera.pdf
  
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- Clarification about What does this have to do with the brain video _ Coursera.pdf
  
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Week1
- 01-Train Dev Test sets.mp4
  
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- 02-Bias Variance.mp4
  
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- 03-Basic Recipe for Machine Learning .mp4
  
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- 04-Regularization.mp4
  
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- 05-Why regularization reduces overfitting.mp4
  
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- 06-Dropout Regularization .mp4
  
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- 07-Understanding Dropout.mp4
  
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- 08-Other regularization methods.mp4
  
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- 09-Normalizing inputs.mp4
  
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- 10-Vanishing Exploding gradients.mp4
  
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- 11-Weight Initialization for Deep Networks.mp4
  
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- 12-Numerical approximation of gradients.mp4
  
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- 13-Gradient checking.mp4
  
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- 14-Gradient Checking Implementation Notes .mp4
  
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- 15-Yoshua Bengio interview.mp4
  
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datasets
- data.mat
  
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- test_catvnoncat.h5
  
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- train_catvnoncat.h5
  
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images
- dropout1_kiank.mp4
  
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- dropout2_kiank.mp4
  
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- field_kiank.png
  
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- reg_utils.py
  
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- Regularization - v2.ipynb
  
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- Regularization_v2a.ipynb
  
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- Regularization.ipynb
  
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- testCases.py
  
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images
- 1Dgrad_kiank.png
  
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- dictionary_to_vector.png
  
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- handbackward_kiank.png
  
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- handforward_kiank.png
  
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- NDgrad_kiank.png
  
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- testCases.py
  
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- Clarification about Upcoming Normalizing Inputs Video _ Coursera.pdf
  
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- Clarification about Upcoming Regularization Video _ Coursera.pdf
  
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- Clarification about Upcoming Understanding dropout Video _ Coursera.pdf
  
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- Practical aspects of deep learning _ Coursera.mhtml
  
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Week2
- 01-Mini-batch gradient descent.mp4
  
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- 02-Understanding mini-batch gradient descent.mp4
  
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- 03-Exponentially weighted averages.mp4
  
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- 04-Understanding exponentially weighted averages .mp4
  
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- 05-Bias correction in exponentially weighted averages.mp4
  
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- 06-Gradient descent with momentum.mp4
  
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- 07-RMSprop.mp4
  
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- 08-Adam optimization algorithm.mp4
  
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- 09-Learning rate decay.mp4
  
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- 10-The problem of local optima.mp4
  
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- 11-Yuanqing Lin interview.mp4
  
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datasets
- data.mat
  
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images
- cost.jpg
  
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- kiank_minibatch.png
  
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- kiank_partition.png
  
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- kiank_sgd.png
  
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- kiank_shuffle.png
  
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- Momentum.png
  
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- opt_momentum.png
  
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- opt1.gif
  
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- opt2.gif
  
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- opt_utils_v1a.py
  
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- opt_utils.py
  
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- Optimization methods.ipynb
  
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- Optimization_methods_v1b.ipynb
  
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- testCases.py
  
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- Clarification about Learning Rate Decay Video _ Coursera.pdf
  
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- Clarification about Upcoming Adam Optimization Video _ Coursera.pdf
  
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Quiz
- Optimization algorithms _ Coursera.mhtml
  
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Week3
- 01-Tuning process.mp4
  
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- 02-Using an appropriate scale to pick hyperparameters.mp4
  
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- 03-Hyperparameters tuning in practice- Pandas vs. Caviar.mp4
  
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- 04-Normalizing activations in a network.mp4
  
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- 05-Fitting Batch Norm into a neural network .mp4
  
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- 06-Why does Batch Norm work.mp4
  
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- 07-Batch Norm at test time.mp4
  
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- 08-Softmax Regression.mp4
  
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- 09-Training a softmax classifier.mp4
  
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- 10-Deep learning frameworks.mp4
  
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- 11-TensorFlow.mp4
  
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datasets
- test_signs.h5
  
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images
- hands.png
  
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- onehot.png
  
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- thumbs_up.jpg
  
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- improv_utils.py
  
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- Tensorflow Tutorial v1.ipynb
  
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- Tensorflow Tutorial v2.ipynb
  
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- Tensorflow Tutorial v3.ipynb
  
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- Tensorflow Tutorial v3a.ipynb
  
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- TensorFlow_Tutorial_v3b.ipynb
  
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- tf_utils.py
  
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- Clarifications about Upcoming Softmax Video _ Coursera.pdf
  
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- Note about TensorFlow 1 and TensorFlow 2 _ Coursera.pdf
  
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Quiz
- Hyperparameter tuning, Batch Normalization, Programming Frameworks _ Coursera.mhtml
  
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Week1
- 01-Why ML Strategy.mp4
  
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- 02-Orthogonalization.mp4
  
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- 03-Single number evaluation metric.mp4
  
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- 04-Satisficing and Optimizing metric.mp4
  
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- 05-Train dev test distributions.mp4
  
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- 06-Size of the dev and test sets.mp4
  
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- 07-When to change dev test sets and metrics.mp4
  
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- 08-Why human-level performance.mp4
  
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- 09-Avoidable bias.mp4
  
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- 10-Understanding human-level performance.mp4
  
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- 11-Surpassing human-level performance.mp4
  
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- 12-Improving your model performance.mp4
  
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- 13-Andrej Karpathy interview.mp4
  
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- Machine Learning flight simulator _ Coursera.pdf
  
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Quiz
- Bird recognition in the city of Peacetopia (case study) _ Coursera.mhtml
  
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Week2
- 01-Carrying out error analysis.mp4
  
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- 02-Cleaning up incorrectly labeled data.mp4
  
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- 03-Build your first system quickly, then iterate .mp4
  
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- 04-Training and testing on different distributions.mp4
  
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- 05-Bias and Variance with mismatched data distributions.mp4
  
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- 06-Addressing data mismatch.mp4
  
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- 07-Transfer learning.mp4
  
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- 08-Multi-task learning.mp4
  
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- 09-What is end-to-end deep learning.mp4
  
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- 10-Whether to use end-to-end deep learning.mp4
  
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- 11-Ruslan Salakhutdinov interview.mp4
  
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Quiz
- Autonomous driving (case study) _ Coursera.mhtml
  
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Week1
- 01-Computer Vision.mp4
  
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- 02-Edge Detection Example.mp4
  
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- 03-More Edge Detection.mp4
  
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- 04-Padding.mp4
  
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- 05-Strided Convolutions.mp4
  
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- 06-Convolutions Over Volume.mp4
  
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- 07-One Layer of a Convolutional Network.mp4
  
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- 08-Simple Convolutional Network Example .mp4
  
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- 09-Pooling Layers.mp4
  
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- 10-CNN Example.mp4
  
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- 11-Why Convolutions.mp4
  
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- 12-Yann LeCun Interview.mp4
  
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datasets
- test_signs.h5
  
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images
- a_pool.png
  
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- ave_pool1.png
  
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- ave-pool.png
  
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- average_pool.png
  
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- conv_kiank.mp4
  
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- conv_nn.png
  
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- conv.png
  
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- conv1.png
  
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- Convolution_schematic.gif
  
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- max_pool.png
  
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- max_pool1.png
  
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- model.png
  
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- PAD.png
  
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- SIGNS.png
  
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- thumbs_up.jpg
  
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- vert_horiz_kiank.png
  
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- CNN Example _CORRECTION_ _ Coursera.pdf
  
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Quiz
- The basics of ConvNets _ Coursera.mhtml
  
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- Simple Convolutional Network Example _CORRECTION_ _ Coursera.pdf
  
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- Strided convolutions _CORRECTION_ _ Coursera.pdf
  
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- Why Convolutions_ _CORRECTION_ _ Coursera.pdf
  
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Week2
- 01-Why look at case studies.mp4
  
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- 02-Classic Networks.mp4
  
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- 03-ResNets.mp4
  
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- 04-Why ResNets Work.mp4
  
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- 05-Networks in Networks and 1x1 Convolutions.mp4
  
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- 06-Inception Network Motivation.mp4
  
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- 07-Inception Network.mp4
  
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- 08-Using Open-Source Implementation.mp4
  
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- 09-Transfer Learning.mp4
  
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- 10-Data Augmentation.mp4
  
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- 11-State of Computer Vision.mp4
  
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datasets
- test_happy.h5
  
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- train_happy.h5
  
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images
- face_images.png
  
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- happy-house.jpg
  
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- house-members.png
  
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- my_image.jpg
  
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- Keras - Tutorial - Happy House v1.ipynb
  
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- Keras - Tutorial - Happy House v2.ipynb
  
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- Keras_Tutorial_v2a.ipynb
  
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- kt_utils.py
  
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datasets
- test_signs.h5
  
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images
- convblock_kiank.png
  
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- idblock2_kiank.png
  
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- idblock3_kiank.png
  
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- my_image.jpg
  
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- resnet_kiank.png
  
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- signs_data_kiank.png
  
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- skip_connection_kiank.png
  
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- vanishing_grad_kiank.png
  
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- model.png
  
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- Residual Networks - v1.ipynb
  
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- Residual Networks - v2.ipynb
  
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- Residual_Networks_v2a.ipynb
  
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- resnets_utils.py
  
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- Inception Network Motivation _CORRECTION_ _ Coursera.pdf
  
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Quiz
- Deep convolutional models _ Coursera.mhtml
  
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Week3
- 01-Object Localization.mp4
  
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- 02-Landmark Detection.mp4
  
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- 03-Object Detection.mp4
  
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- 04-Convolutional Implementation of Sliding Windows.mp4
  
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- 05-Bounding Box Predictions.mp4
  
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- 06-Intersection Over Union.mp4
  
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- 07-Non-max Suppression.mp4
  
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- 08-Anchor Boxes.mp4
  
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- 09-YOLO Algorithm.mp4
  
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- 10-(Optional) Region Proposals.mp4
  
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font
- FiraMono-Medium.otf
  
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images
- 0011.jpg
  
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- 0120.jpg
  
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- giraffe.jpg
  
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- test.jpg
  
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- LICENSE
  
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nb_images
- anchor_map.png
  
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- architecture.png
  
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- box_label.png
  
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- driveai.png
  
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- flatten.png
  
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- iou.png
  
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- non-max-suppression.png
  
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- pred_video_compressed2.mp4
  
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- pred_video.mp4
  
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- prediction_video_compressed.mp4
  
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- proba_map.png
  
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- probability_extraction.png
  
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- road_video_compressed.mp4
  
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- road_video_compressed2.mp4
  
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- road_video.mp4
  
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out
- test.jpg
  
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utils
- __init__.py
  
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Quiz
- Detection algorithms _ Coursera.mhtml
  
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- YOLO algorithm _CORRECTION_ _ Coursera.pdf
  
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Week4
- 01-What is face recognition.mp4
  
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- 02-One Shot Learning.mp4
  
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- 03-Siamese Network.mp4
  
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- 04-Triplet Loss.mp4
  
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- 05-Face Verification and Binary Classification .mp4
  
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- 06-What is neural style transfer.mp4
  
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- 07-What are deep ConvNets learning.mp4
  
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- 08-Cost Function.mp4
  
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- 09-Content Cost Function.mp4
  
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- 10-Style Cost Function.mp4
  
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- 11-1D and 3D Generalizations.mp4
  
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datasets
- test_happy.h5
  
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- train_happy.h5
  
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- Face Recognition for the Happy House - v1.ipynb
  
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- Face Recognition for the Happy House - v2.ipynb
  
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- Face Recognition for the Happy House - v3.ipynb
  
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- Face_Recognition_v3a.ipynb
  
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- fr_utils.py
  
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images
- andrew.jpg
  
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- arnaud.jpg
  
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- benoit.jpg
  
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- bertrand.jpg
  
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- camera_0.jpg
  
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- camera_1.jpg
  
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- camera_2.jpg
  
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- camera_3.jpg
  
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- camera_4.jpg
  
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- camera_5.jpg
  
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- dan.jpg
  
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- danielle.png
  
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- distance_kiank.png
  
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- distance_matrix.png
  
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- f_x.png
  
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- felix.jpg
  
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- inception_block1a.png
  
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- kevin.jpg
  
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- kian.jpg
  
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- pixel_comparison.png
  
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- sebastiano.jpg
  
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- tian.jpg
  
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- triplet_comparison.png
  
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- younes.jpg
  
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- inception_blocks_v2.py
  
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weights
- bn1_b.csv
  
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- bn1_m.csv
  
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- bn1_v.csv
  
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- bn1_w.csv
  
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- bn2_b.csv
  
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- bn2_m.csv
  
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- bn2_v.csv
  
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- bn2_w.csv
  
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- bn3_b.csv
  
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- bn3_m.csv
  
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- bn3_v.csv
  
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- bn3_w.csv
  
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- conv1_b.csv
  
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- conv1_w.csv
  
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- conv2_b.csv
  
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- conv2_w.csv
  
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- conv3_b.csv
  
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- conv3_w.csv
  
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- dense_b.csv
  
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- dense_w.csv
  
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- inception_3a_1x1_bn_b.csv
  
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- inception_3a_1x1_bn_m.csv
  
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- inception_3a_1x1_bn_v.csv
  
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- inception_3a_1x1_bn_w.csv
  
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- inception_3a_1x1_conv_b.csv
  
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- inception_3a_1x1_conv_w.csv
  
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- inception_3a_3x3_bn1_b.csv
  
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- inception_3a_3x3_bn1_m.csv
  
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- inception_3a_3x3_bn1_v.csv
  
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- inception_3a_3x3_bn1_w.csv
  
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- inception_3a_3x3_bn2_b.csv
  
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- inception_3a_3x3_bn2_m.csv
  
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- inception_3a_3x3_bn2_v.csv
  
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- inception_3a_3x3_bn2_w.csv
  
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- inception_3a_3x3_conv1_b.csv
  
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- inception_3a_3x3_conv1_w.csv
  
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- inception_3a_3x3_conv2_b.csv
  
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- inception_3a_3x3_conv2_w.csv
  
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- inception_3a_5x5_bn1_b.csv
  
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- inception_3a_5x5_bn1_m.csv
  
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- inception_3a_5x5_bn1_v.csv
  
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- inception_3a_5x5_bn1_w.csv
  
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- inception_3a_5x5_bn2_b.csv
  
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- inception_3a_5x5_bn2_m.csv
  
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- inception_3a_5x5_bn2_v.csv
  
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- inception_3a_5x5_bn2_w.csv
  
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- inception_3a_5x5_conv1_b.csv
  
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- inception_3a_5x5_conv1_w.csv
  
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- inception_3a_5x5_conv2_b.csv
  
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- inception_3a_5x5_conv2_w.csv
  
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- inception_3a_pool_bn_b.csv
  
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- inception_3a_pool_bn_m.csv
  
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- inception_3a_pool_bn_v.csv
  
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- inception_3a_pool_bn_w.csv
  
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- inception_3a_pool_conv_b.csv
  
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- inception_3a_pool_conv_w.csv
  
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- inception_3b_1x1_bn_b.csv
  
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- inception_3b_1x1_bn_m.csv
  
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- inception_3b_1x1_bn_v.csv
  
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- inception_3b_1x1_bn_w.csv
  
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- inception_3b_1x1_conv_b.csv
  
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- inception_3b_1x1_conv_w.csv
  
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- inception_3b_3x3_bn1_b.csv
  
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- inception_3b_3x3_bn1_m.csv
  
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- inception_3b_3x3_bn1_v.csv
  
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- inception_3b_3x3_bn1_w.csv
  
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- inception_3b_3x3_bn2_b.csv
  
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- inception_3b_3x3_bn2_m.csv
  
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- inception_3b_3x3_bn2_v.csv
  
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- inception_3b_3x3_bn2_w.csv
  
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- inception_3b_3x3_conv1_b.csv
  
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- inception_3b_3x3_conv1_w.csv
  
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- inception_3b_3x3_conv2_b.csv
  
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- inception_3b_3x3_conv2_w.csv
  
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- inception_3b_5x5_bn1_b.csv
  
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- inception_3b_5x5_bn1_m.csv
  
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- inception_3b_5x5_bn1_v.csv
  
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- inception_3b_5x5_bn1_w.csv
  
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- inception_3b_5x5_bn2_b.csv
  
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- inception_3b_5x5_bn2_m.csv
  
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- inception_3b_5x5_bn2_v.csv
  
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- inception_3b_5x5_bn2_w.csv
  
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- inception_3b_5x5_conv1_b.csv
  
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- inception_3b_5x5_conv1_w.csv
  
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- inception_3b_5x5_conv2_b.csv
  
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- inception_3b_5x5_conv2_w.csv
  
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- inception_3b_pool_bn_b.csv
  
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- inception_3b_pool_bn_m.csv
  
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- inception_3b_pool_bn_v.csv
  
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- inception_3b_pool_bn_w.csv
  
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- inception_3b_pool_conv_b.csv
  
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- inception_3b_pool_conv_w.csv
  
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- inception_3c_3x3_bn1_b.csv
  
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- inception_3c_3x3_bn1_m.csv
  
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- inception_3c_3x3_bn1_v.csv
  
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- inception_3c_3x3_bn1_w.csv
  
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- inception_3c_3x3_bn2_b.csv
  
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- inception_3c_3x3_bn2_m.csv
  
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- inception_3c_3x3_bn2_v.csv
  
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- inception_3c_3x3_bn2_w.csv
  
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- inception_3c_3x3_conv1_b.csv
  
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- inception_3c_3x3_conv1_w.csv
  
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- inception_3c_3x3_conv2_b.csv
  
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- inception_3c_3x3_conv2_w.csv
  
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- inception_3c_5x5_bn1_b.csv
  
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- inception_3c_5x5_bn1_m.csv
  
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- inception_3c_5x5_bn1_v.csv
  
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- inception_3c_5x5_bn1_w.csv
  
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- inception_3c_5x5_bn2_b.csv
  
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- inception_3c_5x5_bn2_m.csv
  
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- inception_3c_5x5_bn2_v.csv
  
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- inception_3c_5x5_bn2_w.csv
  
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- inception_3c_5x5_conv1_b.csv
  
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- inception_3c_5x5_conv1_w.csv
  
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- inception_3c_5x5_conv2_b.csv
  
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- inception_3c_5x5_conv2_w.csv
  
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- inception_4a_1x1_bn_b.csv
  
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- inception_4a_1x1_bn_m.csv
  
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- inception_4a_1x1_bn_v.csv
  
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- inception_4a_1x1_bn_w.csv
  
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- inception_4a_1x1_conv_b.csv
  
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- inception_4a_1x1_conv_w.csv
  
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- inception_4a_3x3_bn1_b.csv
  
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- inception_4a_3x3_bn1_m.csv
  
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- inception_4a_3x3_bn1_v.csv
  
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- inception_4a_3x3_bn1_w.csv
  
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- inception_4a_3x3_bn2_b.csv
  
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- inception_4a_3x3_bn2_m.csv
  
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- inception_4a_3x3_bn2_v.csv
  
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- inception_4a_3x3_bn2_w.csv
  
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- inception_4a_3x3_conv1_b.csv
  
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- inception_4a_3x3_conv1_w.csv
  
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- inception_4a_3x3_conv2_b.csv
  
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- inception_4a_3x3_conv2_w.csv
  
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- inception_4a_5x5_bn1_b.csv
  
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- inception_4a_5x5_bn1_m.csv
  
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- inception_4a_5x5_bn1_v.csv
  
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- inception_4a_5x5_bn1_w.csv
  
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- inception_4a_5x5_bn2_b.csv
  
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- inception_4a_5x5_bn2_m.csv
  
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- inception_4a_5x5_bn2_v.csv
  
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- inception_4a_5x5_bn2_w.csv
  
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- inception_4a_5x5_conv1_b.csv
  
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- inception_4a_5x5_conv1_w.csv
  
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- inception_4a_5x5_conv2_b.csv
  
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- inception_4a_5x5_conv2_w.csv
  
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- inception_4a_pool_bn_b.csv
  
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- inception_4a_pool_bn_m.csv
  
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- inception_4a_pool_bn_v.csv
  
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- inception_4a_pool_bn_w.csv
  
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- inception_4a_pool_conv_b.csv
  
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- inception_4a_pool_conv_w.csv
  
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- inception_4e_3x3_bn1_b.csv
  
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- inception_4e_3x3_bn1_m.csv
  
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- inception_4e_3x3_bn1_v.csv
  
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- inception_4e_3x3_bn1_w.csv
  
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- inception_4e_3x3_bn2_b.csv
  
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- inception_4e_3x3_bn2_m.csv
  
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- inception_4e_3x3_bn2_v.csv
  
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- inception_4e_3x3_bn2_w.csv
  
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- inception_4e_3x3_conv1_b.csv
  
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- inception_4e_3x3_conv1_w.csv
  
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- inception_4e_3x3_conv2_b.csv
  
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- inception_4e_3x3_conv2_w.csv
  
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- inception_4e_5x5_bn1_b.csv
  
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- inception_4e_5x5_bn1_m.csv
  
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- inception_4e_5x5_bn1_v.csv
  
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- inception_4e_5x5_bn1_w.csv
  
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- inception_4e_5x5_bn2_b.csv
  
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- inception_4e_5x5_bn2_m.csv
  
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- inception_4e_5x5_bn2_v.csv
  
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- inception_4e_5x5_bn2_w.csv
  
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- inception_4e_5x5_conv1_b.csv
  
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- inception_4e_5x5_conv1_w.csv
  
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- inception_4e_5x5_conv2_b.csv
  
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- inception_4e_5x5_conv2_w.csv
  
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- inception_5a_1x1_bn_b.csv
  
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- inception_5a_1x1_bn_m.csv
  
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- inception_5a_1x1_bn_v.csv
  
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- inception_5a_1x1_bn_w.csv
  
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- inception_5a_1x1_conv_b.csv
  
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- inception_5a_1x1_conv_w.csv
  
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- inception_5a_3x3_bn1_b.csv
  
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- inception_5a_3x3_bn1_m.csv
  
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- inception_5a_3x3_bn1_v.csv
  
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- inception_5a_3x3_bn1_w.csv
  
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- inception_5a_3x3_bn2_b.csv
  
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- inception_5a_3x3_bn2_m.csv
  
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- inception_5a_3x3_bn2_v.csv
  
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- inception_5a_3x3_bn2_w.csv
  
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- inception_5a_3x3_conv1_b.csv
  
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- inception_5a_3x3_conv1_w.csv
  
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- inception_5a_3x3_conv2_b.csv
  
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- inception_5a_3x3_conv2_m.csv
  
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- inception_5a_3x3_conv2_v.csv
  
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- inception_5a_3x3_conv2_w.csv
  
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- inception_5a_pool_bn_b.csv
  
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- inception_5a_pool_bn_m.csv
  
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- inception_5a_pool_bn_v.csv
  
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- inception_5a_pool_bn_w.csv
  
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- inception_5a_pool_conv_b.csv
  
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- inception_5a_pool_conv_w.csv
  
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- inception_5b_1x1_bn_b.csv
  
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- inception_5b_1x1_bn_m.csv
  
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- inception_5b_1x1_bn_v.csv
  
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- inception_5b_1x1_bn_w.csv
  
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- inception_5b_1x1_conv_b.csv
  
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- inception_5b_1x1_conv_w.csv
  
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- inception_5b_3x3_bn1_b.csv
  
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- inception_5b_3x3_bn1_m.csv
  
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- inception_5b_3x3_bn1_v.csv
  
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- inception_5b_3x3_bn1_w.csv
  
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- inception_5b_3x3_bn2_b.csv
  
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- inception_5b_3x3_bn2_m.csv
  
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- inception_5b_3x3_bn2_v.csv
  
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- inception_5b_3x3_bn2_w.csv
  
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- inception_5b_3x3_conv1_b.csv
  
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- inception_5b_3x3_conv1_w.csv
  
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- inception_5b_3x3_conv2_b.csv
  
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- inception_5b_3x3_conv2_w.csv
  
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- inception_5b_pool_bn_b.csv
  
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- inception_5b_pool_bn_m.csv
  
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- inception_5b_pool_bn_v.csv
  
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- inception_5b_pool_bn_w.csv
  
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- inception_5b_pool_conv_b.csv
  
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- inception_5b_pool_conv_w.csv
  
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images
- camp-nou.jpg
  
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- cat.jpg
  
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- circle_abstract.png
  
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- claude-monet.jpg
  
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- content_plus_style.png
  
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- content.jpeg
  
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- content300.jpg
  
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- drop-of-water.jpg
  
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- gram.png
  
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- hidden_layers.png
  
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- louvre_generated.png
  
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- louvre_small.jpg
  
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- louvre.jpg
  
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- monet_800600.jpg
  
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- monet.jpg
  
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- NST_GM.png
  
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- NST_LOSS.png
  
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- pasargad_kashi.png
  
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- persian_cat_content.jpg
  
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- persian_cat.jpg
  
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- perspolis_vangogh.png
  
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- reshape_loss.png
  
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- result.png
  
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- sandstone.jpg
  
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- stone_style.jpg
  
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- style300.jpg
  
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- nst_utils.py
  
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output
- 0.png
  
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- 20.png
  
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- 40.png
  
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- 60.png
  
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- 80.png
  
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- 100.png
  
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- 120.png
  
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- 140.png
  
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- 160.png
  
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- 180.png
  
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- generated_image.jpg
  
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pretrained-model
- Face Verification and Binary Classification _CORRECTION_ _ Coursera.pdf
  
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Quiz
- Special applications_ Face recognition _ Neural style transfer _ Coursera.mhtml
  
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- Style Cost _CORRECTION_ _ Coursera.pdf
  
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- Triplet Loss _CORRECTION_ _ Coursera.pdf
  
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Week1
- 01-Why sequence models.mp4
  
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- 02-Notation.mp4
  
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- 03-Recurrent Neural Network Model.mp4
  
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- 04-Backpropagation through time.mp4
  
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- 05-Different types of RNNs .mp4
  
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- 06-Language model and sequence generation.mp4
  
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- 07-Sampling novel sequences.mp4
  
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- 08-Vanishing gradients with RNNs.mp4
  
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- 10-Gated Recurrent Unit (GRU).mp4
  
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- 11-Long Short Term Memory (LSTM).mp4
  
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- 12-Bidirectional RNN.mp4
  
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- 13-Deep RNNs.mp4
  
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images
- initial_state.png
  
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- LSTM_cell_backward_rev3a_5.png
  
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- LSTM_cell_backward_rev3a_c2.png
  
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- LSTM_figure4_v3a.png
  
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- LSTM_rnn.png
  
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- LSTM.png
  
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- rnn_backward_overview_3a_1.png
  
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- rnn_cell_backprop.png
  
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- rnn_cell_backward_3a_4.png
  
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- rnn_cell_backward_3a_c.png
  
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- rnn_forward_sequence_figure3_v3a.png
  
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- rnn_step_forward_figure2_v3a.png
  
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- rnn_step_forward.png
  
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- rnn.png
  
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- RNN(1).png
  
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images
- clip.png
  
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- dino.jpg
  
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- dinos3.png
  
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- mangosaurus.jpeg
  
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- rnn.png
  
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- shakespeare.jpg
  
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models
- model_shakespeare_kiank_350_epoch.h5
  
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- model_shakespeare_kiank.h5
  
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data
- 30s_seq.mp3
  
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- 30s_trained_model.mp3
  
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- deepjazz_on_metheny...128_epochs.midi
  
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- original_metheny.mid
  
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- training_example.mp3
  
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- data_utils.py
  
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- grammar.py
  
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images
- jazz.jpg
  
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- model.png
  
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- music_gen.png
  
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- music_generation.png
  
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- Improvise a Jazz Solo with an LSTM Network - v3.ipynb
  
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- Improvise_a_Jazz_Solo_with_an_LSTM_Network_v3a.ipynb
  
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- inference_code.py
  
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- midi.py
  
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- music_utils.py
  
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output
- my_music.midi
  
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- preprocess.py
  
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Quiz
- Recurrent Neural Networks _ Coursera.mht
  
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Week2
- 01-Word Representation.mp4
  
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- 02-Using word embeddings.mp4
  
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- 03-Properties of word embeddings.mp4
  
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- 04-Embedding matrix.mp4
  
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- 05-Learning word embeddings.mp4
  
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- 06-Word2Vec.mp4
  
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- 07-Negative Sampling.mp4
  
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- 08-GloVe word vectors.mp4
  
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- 09-Sentiment Classification.mp4
  
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- 10-Debiasing word embeddings.mp4
  
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data
- emojify_data.csv
  
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- tes.csv
  
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- tess.csv
  
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- tesss.csv
  
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- test_emoji.csv
  
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- testing.csv
  
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- train_emoji.csv
  
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- emo_utils.py
  
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- Emojify - v2.ipynb
  
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- Emojify_v2a.ipynb
  
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images
- data_set.png
  
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- dataset_kiank.png
  
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- emb_kiank.png
  
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- embedding1.png
  
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- emo_model.png
  
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- emojifier-v2.png
  
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- emojifierv1.png
  
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- emojify_model.png
  
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- emojiss.png
  
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- image_1.png
  
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- woebot.png
  
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images
- 1-hot-vector.png
  
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- cosine_sim.png
  
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- equalize10.png
  
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- lookup.png
  
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- neutral.png
  
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- neutralize_kiank.png
  
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- neutralize.png
  
  Preview
- Operations on word vectors - v2.ipynb
  
  Preview
- Operations_on_word_vectors_v2a.ipynb
  
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Quiz
- Natural Language Processing _ Word Embeddings _ Coursera.mht
  
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Week3
- 01-Basic Models.mp4
  
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- 02-Picking the most likely sentence.mp4
  
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- 03-Beam Search.mp4
  
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- 04-Refinements to Beam Search.mp4
  
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- 05-Error analysis in beam search.mp4
  
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- 06-Bleu Score (optional).mp4
  
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- 07-Attention Model Intuition.mp4
  
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- 08-Attention Model.mp4
  
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- 09-Speech recognition.mp4
  
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- 10-Trigger Word Detection.mp4
  
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- 11-Conclusion and thank you.mp4
  
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images
- attn_mechanism.png
  
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- attn_model.png
  
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- date_attention.png
  
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- date_attention2.png
  
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- poorly_trained_model.png
  
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- table.png
  
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models
- model_kiank.h5
  
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- model_sisi_oklm.h5
  
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- model.h5
  
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- oklmkiank.h5
  
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- Neural machine translation with attention - v4.ipynb
  
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- Neural_machine_translation_with_attention_v4a.ipynb
  
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- nmt_utils.py
  
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audio_examples
- chime.wav
  
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- example_train.wav
  
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- insert_reference.wav
  
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- my_audio.wav
  
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- train_reference.wav
  
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- chime_output.wav
  
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images
- label_diagram.png
  
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- model.png
  
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- ones_reference.png
  
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- sound.png
  
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- spectrogram.png
  
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- train_label.png
  
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- train_reference.png
  
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- insert_test.wav
  
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activates
- 1_act2.wav
  
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- 1_act3.wav
  
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- 1.wav
  
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- 2_act2.wav
  
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- 2_act3.wav
  
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- 2.wav
  
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- 3_act2.wav
  
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- 3_act3.wav
  
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- 3.wav
  
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- 4_act2.wav
  
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backgrounds
- 1.wav
  
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- 2.wav
  
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dev
- 1.wav
  
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- 2.wav
  
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negatives
- 1_0.wav
  
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- 1.wav
  
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- 2_1.wav
  
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- 2.wav
  
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- 3_2.wav
  
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- 3.wav
  
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- 4_0.wav
  
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- 4.wav
  
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- 5_1.wav
  
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- 5.wav
  
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- td_utils.py
  
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- train.wav
  
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- Trigger word detection - v1.ipynb
  
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XY_dev
- Y_dev.npy
  
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XY_train
- Y.npy
  
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- Bleu Score _CORRECTION_ _ Coursera.pdf
  
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- Corrections _ Coursera.pdf
  
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- Coursera Deep Learning Module 5 Week 3 Notes _ XAI - eXplainable AI.mht
  
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- Instructions if you are unable to open your notebook _ Coursera.pdf
  
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Quiz
- Sequence models _ Attention mechanism _ Coursera.mht
  
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- Workera_s Standardized Tests for AI Skills _ Coursera.pdf
  
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