Deep Learning Specialization - Andrew

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Course content

01. Neural Network and Deep Learning
- deep-learning-notation.pdf
Week1
- 01-welcome.mp4
- 02-what-is-neural-network.mp4
- 03-supervised-learning-with-neural-network.mp4
- 04-why-is-deeplearning-taking-off.mp4
- 05-about-this-course.mp4
- 06-course-resourse.mp4
- 08-hero-interview.mp4
- Frequently Asked Questions _ Coursera.pdf
- How to use Discussion Forums _ Coursera.pdf
Quiz
- Introduction to deep learning _ Coursera.mhtml
Week2
- 01-Binary Classification.mp4
- 02-Logistic Regression.mp4
- 03-Logistic Regression Cost Function.mp4
- 04-Gradient Descent.mp4
- 05-Derivatives.mp4
- 06-More Derivative Examples.mp4
- 07-Computation graph.mp4
- 08-Derivatives with a Computation Graph.mp4
- 09-Logistic Regression Gradient Descent.mp4
- 10-Gradient Descent on m Examples.mp4
- 11-Vectorization.mp4
- 12-More Vectorization Examples .mp4
- 13-Vectorizing Logistic Regression.mp4
- 14-Vectorizing Logistic Regression_s Gradient Output.mp4
- 15-Broadcasting in Python .mp4
- 16-A note on python numpy vectors.mp4
- 17-Quick tour of Jupyter-iPython Notebooks.mp4
- 18-Explanation of logistic regression cost function (optional).mp4
- 19-Pieter Abbeel interview.mp4
Assignment
- Deep Learning Honor Code _ Coursera.pdf
images
- image2vector_kiank.png
- image2vector.png
- Sigmoid.png
- Python Basics With Numpy v3.ipynb
- Python_Basics_With_Numpy_v3a.ipynb
datasets
- test_catvnoncat.h5
- train_catvnoncat.h5
images
- cat_in_iran.jpg
- gargouille.jpg
- image1.png
- image2.png
- la_defense.jpg
- LogReg_kiank.png
- my_image.jpg
- my_image2.jpg
- Logistic Regression with a Neural Network mindset v4 - Stanford.ipynb
- Logistic Regression with a Neural Network mindset v4.ipynb
- Logistic Regression with a Neural Network mindset v5.ipynb
- Logistic_Regression_with_a_Neural_Network_mindset_v6a.ipynb
- lr_utils.py
- Programming Assignment FAQ _ Coursera.pdf
- Clarification about Upcoming Gradient Descent Video _ Coursera.pdf
- Clarification about Upcoming Logistic Regression Cost Function Video _ Coursera.pdf
- Clarification of _dz_ _ Coursera.pdf
- Copy of Clarification about Upcoming Logistic Regression Cost Function Video _ Coursera.pdf
- Derivation of DL_dz (optional reading) _ Coursera.pdf
Week3
- 01-Neural Networks Overview.mp4
- 02-Neural Network Representation .mp4
- 03-Computing a Neural Network_s Output.mp4
- 04-Vectorizing across multiple examples.mp4
- 05-Explanation for Vectorized Implementation.mp4
- 06-Activation functions.mp4
- 07-Why do you need non-linear activation functions.mp4
- 08-Derivatives of activation functions.mp4
- 09-Gradient descent for Neural Networks.mp4
- 10-Backpropagation intuition (optional).mp4
- 11-Random Initialization.mp4
- 12-Ian Goodfellow interview.mp4
images
- classification_kiank.png
- grad_summary.png
- sgd_bad.gif
- sgd.gif
- Planar data classification with one hidden layer v4.ipynb
- Planar data classification with one hidden layer v5.ipynb
- Planar_data_classification_with_onehidden_layer_v6a.ipynb
- Planar_data_classification_with_onehidden_layer_v6b.ipynb
- Planar_data_classification_with_onehidden_layer_v6c.ipynb
- planar_utils.py
- testCases_v2.py
- Clarification about Upcoming Backpropagation intuition (optional) _ Coursera.pdf
- Clarification_ Activation Function _ Coursera.pdf
- FlowerModel.png
Week4
- 01-Deep L-layer neural network.mp4
- 02-Forward Propagation in a Deep Network.mp4
- 03-Getting your matrix dimensions right .mp4
- 04-Why deep representations.mp4
- 05-Building blocks of deep neural networks .mp4
- 06-Forward and Backward Propagation.mp4
- 07-Parameters vs Hyperparameters.mp4
- 08-What does this have to do with the brain.mp4
images
- 2layerNN.png
- backpass.png
- backprop_kiank.png
- backprop.png
- final outline.png
- imvector.png
- linearback_kiank.png
- mn_backward.png
- model_architecture_kiank.png
- model_architecture2.png
- n_model_backward.png
- NlayerNN.png
- nm_backward.png
- relu.png
- structure.png
- testCases_v3.py
- testCases_v4.py
- testCases_v4a.py
datasets
- test_catvnoncat.h5
- train_catvnoncat.h5
- Deep Neural Network - Application v3.ipynb
- Deep Neural Network - Application v4.ipynb
- Deep Neural Network - Application v5.ipynb
- Deep Neural Network - Application v6.ipynb
- Deep Neural Network - Application v7.ipynb
- Deep Neural Network - Application v8.ipynb
- dnn_app_utils_v2.py
- dnn_app_utils_v3.py
images
- 2layerNN_kiank.png
- imvector.png
- imvectorkiank.png
- LlayerNN_kiank.png
- my_image.jpg
- Clarification about Getting your matrix dimensions right video _ Coursera.pdf
- Clarification about Upcoming Forward and Backward Propagation Video _ Coursera.pdf
- Clarification about What does this have to do with the brain video _ Coursera.pdf
Week1
- 01-Train Dev Test sets.mp4
- 02-Bias Variance.mp4
- 03-Basic Recipe for Machine Learning .mp4
- 04-Regularization.mp4
- 05-Why regularization reduces overfitting.mp4
- 06-Dropout Regularization .mp4
- 07-Understanding Dropout.mp4
- 08-Other regularization methods.mp4
- 09-Normalizing inputs.mp4
- 10-Vanishing Exploding gradients.mp4
- 11-Weight Initialization for Deep Networks.mp4
- 12-Numerical approximation of gradients.mp4
- 13-Gradient checking.mp4
- 14-Gradient Checking Implementation Notes .mp4
- 15-Yoshua Bengio interview.mp4
datasets
- data.mat
- test_catvnoncat.h5
- train_catvnoncat.h5
images
- dropout1_kiank.mp4
- dropout2_kiank.mp4
- field_kiank.png
- reg_utils.py
- Regularization - v2.ipynb
- Regularization_v2a.ipynb
- Regularization.ipynb
- testCases.py
images
- 1Dgrad_kiank.png
- dictionary_to_vector.png
- handbackward_kiank.png
- handforward_kiank.png
- NDgrad_kiank.png
- testCases.py
- Clarification about Upcoming Normalizing Inputs Video _ Coursera.pdf
- Clarification about Upcoming Regularization Video _ Coursera.pdf
- Clarification about Upcoming Understanding dropout Video _ Coursera.pdf
- Practical aspects of deep learning _ Coursera.mhtml
Week2
- 01-Mini-batch gradient descent.mp4
- 02-Understanding mini-batch gradient descent.mp4
- 03-Exponentially weighted averages.mp4
- 04-Understanding exponentially weighted averages .mp4
- 05-Bias correction in exponentially weighted averages.mp4
- 06-Gradient descent with momentum.mp4
- 07-RMSprop.mp4
- 08-Adam optimization algorithm.mp4
- 09-Learning rate decay.mp4
- 10-The problem of local optima.mp4
- 11-Yuanqing Lin interview.mp4
datasets
- data.mat
images
- cost.jpg
- kiank_minibatch.png
- kiank_partition.png
- kiank_sgd.png
- kiank_shuffle.png
- Momentum.png
- opt_momentum.png
- opt1.gif
- opt2.gif
- opt_utils_v1a.py
- opt_utils.py
- Optimization methods.ipynb
- Optimization_methods_v1b.ipynb
- testCases.py
- Clarification about Learning Rate Decay Video _ Coursera.pdf
- Clarification about Upcoming Adam Optimization Video _ Coursera.pdf
Quiz
- Optimization algorithms _ Coursera.mhtml
Week3
- 01-Tuning process.mp4
- 02-Using an appropriate scale to pick hyperparameters.mp4
- 03-Hyperparameters tuning in practice- Pandas vs. Caviar.mp4
- 04-Normalizing activations in a network.mp4
- 05-Fitting Batch Norm into a neural network .mp4
- 06-Why does Batch Norm work.mp4
- 07-Batch Norm at test time.mp4
- 08-Softmax Regression.mp4
- 09-Training a softmax classifier.mp4
- 10-Deep learning frameworks.mp4
- 11-TensorFlow.mp4
datasets
- test_signs.h5
images
- hands.png
- onehot.png
- thumbs_up.jpg
- improv_utils.py
- Tensorflow Tutorial v1.ipynb
- Tensorflow Tutorial v2.ipynb
- Tensorflow Tutorial v3.ipynb
- Tensorflow Tutorial v3a.ipynb
- TensorFlow_Tutorial_v3b.ipynb
- tf_utils.py
- Clarifications about Upcoming Softmax Video _ Coursera.pdf
- Note about TensorFlow 1 and TensorFlow 2 _ Coursera.pdf
Quiz
- Hyperparameter tuning, Batch Normalization, Programming Frameworks _ Coursera.mhtml
Week1
- 01-Why ML Strategy.mp4
- 02-Orthogonalization.mp4
- 03-Single number evaluation metric.mp4
- 04-Satisficing and Optimizing metric.mp4
- 05-Train dev test distributions.mp4
- 06-Size of the dev and test sets.mp4
- 07-When to change dev test sets and metrics.mp4
- 08-Why human-level performance.mp4
- 09-Avoidable bias.mp4
- 10-Understanding human-level performance.mp4
- 11-Surpassing human-level performance.mp4
- 12-Improving your model performance.mp4
- 13-Andrej Karpathy interview.mp4
- Machine Learning flight simulator _ Coursera.pdf
Quiz
- Bird recognition in the city of Peacetopia (case study) _ Coursera.mhtml
Week2
- 01-Carrying out error analysis.mp4
- 02-Cleaning up incorrectly labeled data.mp4
- 03-Build your first system quickly, then iterate .mp4
- 04-Training and testing on different distributions.mp4
- 05-Bias and Variance with mismatched data distributions.mp4
- 06-Addressing data mismatch.mp4
- 07-Transfer learning.mp4
- 08-Multi-task learning.mp4
- 09-What is end-to-end deep learning.mp4
- 10-Whether to use end-to-end deep learning.mp4
- 11-Ruslan Salakhutdinov interview.mp4
Quiz
- Autonomous driving (case study) _ Coursera.mhtml
Week1
- 01-Computer Vision.mp4
- 02-Edge Detection Example.mp4
- 03-More Edge Detection.mp4
- 04-Padding.mp4
- 05-Strided Convolutions.mp4
- 06-Convolutions Over Volume.mp4
- 07-One Layer of a Convolutional Network.mp4
- 08-Simple Convolutional Network Example .mp4
- 09-Pooling Layers.mp4
- 10-CNN Example.mp4
- 11-Why Convolutions.mp4
- 12-Yann LeCun Interview.mp4
datasets
- test_signs.h5
images
- a_pool.png
- ave_pool1.png
- ave-pool.png
- average_pool.png
- conv_kiank.mp4
- conv_nn.png
- conv.png
- conv1.png
- Convolution_schematic.gif
- max_pool.png
- max_pool1.png
- model.png
- PAD.png
- SIGNS.png
- thumbs_up.jpg
- vert_horiz_kiank.png
- CNN Example _CORRECTION_ _ Coursera.pdf
Quiz
- The basics of ConvNets _ Coursera.mhtml
- Simple Convolutional Network Example _CORRECTION_ _ Coursera.pdf
- Strided convolutions _CORRECTION_ _ Coursera.pdf
- Why Convolutions_ _CORRECTION_ _ Coursera.pdf
Week2
- 01-Why look at case studies.mp4
- 02-Classic Networks.mp4
- 03-ResNets.mp4
- 04-Why ResNets Work.mp4
- 05-Networks in Networks and 1x1 Convolutions.mp4
- 06-Inception Network Motivation.mp4
- 07-Inception Network.mp4
- 08-Using Open-Source Implementation.mp4
- 09-Transfer Learning.mp4
- 10-Data Augmentation.mp4
- 11-State of Computer Vision.mp4
datasets
- test_happy.h5
- train_happy.h5
images
- face_images.png
- happy-house.jpg
- house-members.png
- my_image.jpg
- Keras - Tutorial - Happy House v1.ipynb
- Keras - Tutorial - Happy House v2.ipynb
- Keras_Tutorial_v2a.ipynb
- kt_utils.py
datasets
- test_signs.h5
images
- convblock_kiank.png
- idblock2_kiank.png
- idblock3_kiank.png
- my_image.jpg
- resnet_kiank.png
- signs_data_kiank.png
- skip_connection_kiank.png
- vanishing_grad_kiank.png
- model.png
- Residual Networks - v1.ipynb
- Residual Networks - v2.ipynb
- Residual_Networks_v2a.ipynb
- resnets_utils.py
- Inception Network Motivation _CORRECTION_ _ Coursera.pdf
Quiz
- Deep convolutional models _ Coursera.mhtml
Week3
- 01-Object Localization.mp4
- 02-Landmark Detection.mp4
- 03-Object Detection.mp4
- 04-Convolutional Implementation of Sliding Windows.mp4
- 05-Bounding Box Predictions.mp4
- 06-Intersection Over Union.mp4
- 07-Non-max Suppression.mp4
- 08-Anchor Boxes.mp4
- 09-YOLO Algorithm.mp4
- 10-(Optional) Region Proposals.mp4
font
- FiraMono-Medium.otf
images
- 0011.jpg
- 0012.jpg
- 0013.jpg
- 0014.jpg
- 0015.jpg
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- 0099.jpg
- 0103.jpg
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- 0112.jpg
- 0113.jpg
- 0114.jpg
- 0115.jpg
- 0116.jpg
- 0117.jpg
- 0118.jpg
- 0119.jpg
- 0120.jpg
- giraffe.jpg
- test.jpg
- LICENSE
nb_images
- anchor_map.png
- architecture.png
- box_label.png
- driveai.png
- flatten.png
- iou.png
- non-max-suppression.png
- pred_video_compressed2.mp4
- pred_video.mp4
- prediction_video_compressed.mp4
- proba_map.png
- probability_extraction.png
- road_video_compressed.mp4
- road_video_compressed2.mp4
- road_video.mp4
out
- test.jpg
utils
- __init__.py
Quiz
- Detection algorithms _ Coursera.mhtml
- YOLO algorithm _CORRECTION_ _ Coursera.pdf
Week4
- 01-What is face recognition.mp4
- 02-One Shot Learning.mp4
- 03-Siamese Network.mp4
- 04-Triplet Loss.mp4
- 05-Face Verification and Binary Classification .mp4
- 06-What is neural style transfer.mp4
- 07-What are deep ConvNets learning.mp4
- 08-Cost Function.mp4
- 09-Content Cost Function.mp4
- 10-Style Cost Function.mp4
- 11-1D and 3D Generalizations.mp4
datasets
- test_happy.h5
- train_happy.h5
- Face Recognition for the Happy House - v1.ipynb
- Face Recognition for the Happy House - v2.ipynb
- Face Recognition for the Happy House - v3.ipynb
- Face_Recognition_v3a.ipynb
- fr_utils.py
images
- andrew.jpg
- arnaud.jpg
- benoit.jpg
- bertrand.jpg
- camera_0.jpg
- camera_1.jpg
- camera_2.jpg
- camera_3.jpg
- camera_4.jpg
- camera_5.jpg
- dan.jpg
- danielle.png
- distance_kiank.png
- distance_matrix.png
- f_x.png
- felix.jpg
- inception_block1a.png
- kevin.jpg
- kian.jpg
- pixel_comparison.png
- sebastiano.jpg
- tian.jpg
- triplet_comparison.png
- younes.jpg
- inception_blocks_v2.py
weights
- bn1_b.csv
- bn1_m.csv
- bn1_v.csv
- bn1_w.csv
- bn2_b.csv
- bn2_m.csv
- bn2_v.csv
- bn2_w.csv
- bn3_b.csv
- bn3_m.csv
- bn3_v.csv
- bn3_w.csv
- conv1_b.csv
- conv1_w.csv
- conv2_b.csv
- conv2_w.csv
- conv3_b.csv
- conv3_w.csv
- dense_b.csv
- dense_w.csv
- inception_3a_1x1_bn_b.csv
- inception_3a_1x1_bn_m.csv
- inception_3a_1x1_bn_v.csv
- inception_3a_1x1_bn_w.csv
- inception_3a_1x1_conv_b.csv
- inception_3a_1x1_conv_w.csv
- inception_3a_3x3_bn1_b.csv
- inception_3a_3x3_bn1_m.csv
- inception_3a_3x3_bn1_v.csv
- inception_3a_3x3_bn1_w.csv
- inception_3a_3x3_bn2_b.csv
- inception_3a_3x3_bn2_m.csv
- inception_3a_3x3_bn2_v.csv
- inception_3a_3x3_bn2_w.csv
- inception_3a_3x3_conv1_b.csv
- inception_3a_3x3_conv1_w.csv
- inception_3a_3x3_conv2_b.csv
- inception_3a_3x3_conv2_w.csv
- inception_3a_5x5_bn1_b.csv
- inception_3a_5x5_bn1_m.csv
- inception_3a_5x5_bn1_v.csv
- inception_3a_5x5_bn1_w.csv
- inception_3a_5x5_bn2_b.csv
- inception_3a_5x5_bn2_m.csv
- inception_3a_5x5_bn2_v.csv
- inception_3a_5x5_bn2_w.csv
- inception_3a_5x5_conv1_b.csv
- inception_3a_5x5_conv1_w.csv
- inception_3a_5x5_conv2_b.csv
- inception_3a_5x5_conv2_w.csv
- inception_3a_pool_bn_b.csv
- inception_3a_pool_bn_m.csv
- inception_3a_pool_bn_v.csv
- inception_3a_pool_bn_w.csv
- inception_3a_pool_conv_b.csv
- inception_3a_pool_conv_w.csv
- inception_3b_1x1_bn_b.csv
- inception_3b_1x1_bn_m.csv
- inception_3b_1x1_bn_v.csv
- inception_3b_1x1_bn_w.csv
- inception_3b_1x1_conv_b.csv
- inception_3b_1x1_conv_w.csv
- inception_3b_3x3_bn1_b.csv
- inception_3b_3x3_bn1_m.csv
- inception_3b_3x3_bn1_v.csv
- inception_3b_3x3_bn1_w.csv
- inception_3b_3x3_bn2_b.csv
- inception_3b_3x3_bn2_m.csv
- inception_3b_3x3_bn2_v.csv
- inception_3b_3x3_bn2_w.csv
- inception_3b_3x3_conv1_b.csv
- inception_3b_3x3_conv1_w.csv
- inception_3b_3x3_conv2_b.csv
- inception_3b_3x3_conv2_w.csv
- inception_3b_5x5_bn1_b.csv
- inception_3b_5x5_bn1_m.csv
- inception_3b_5x5_bn1_v.csv
- inception_3b_5x5_bn1_w.csv
- inception_3b_5x5_bn2_b.csv
- inception_3b_5x5_bn2_m.csv
- inception_3b_5x5_bn2_v.csv
- inception_3b_5x5_bn2_w.csv
- inception_3b_5x5_conv1_b.csv
- inception_3b_5x5_conv1_w.csv
- inception_3b_5x5_conv2_b.csv
- inception_3b_5x5_conv2_w.csv
- inception_3b_pool_bn_b.csv
- inception_3b_pool_bn_m.csv
- inception_3b_pool_bn_v.csv
- inception_3b_pool_bn_w.csv
- inception_3b_pool_conv_b.csv
- inception_3b_pool_conv_w.csv
- inception_3c_3x3_bn1_b.csv
- inception_3c_3x3_bn1_m.csv
- inception_3c_3x3_bn1_v.csv
- inception_3c_3x3_bn1_w.csv
- inception_3c_3x3_bn2_b.csv
- inception_3c_3x3_bn2_m.csv
- inception_3c_3x3_bn2_v.csv
- inception_3c_3x3_bn2_w.csv
- inception_3c_3x3_conv1_b.csv
- inception_3c_3x3_conv1_w.csv
- inception_3c_3x3_conv2_b.csv
- inception_3c_3x3_conv2_w.csv
- inception_3c_5x5_bn1_b.csv
- inception_3c_5x5_bn1_m.csv
- inception_3c_5x5_bn1_v.csv
- inception_3c_5x5_bn1_w.csv
- inception_3c_5x5_bn2_b.csv
- inception_3c_5x5_bn2_m.csv
- inception_3c_5x5_bn2_v.csv
- inception_3c_5x5_bn2_w.csv
- inception_3c_5x5_conv1_b.csv
- inception_3c_5x5_conv1_w.csv
- inception_3c_5x5_conv2_b.csv
- inception_3c_5x5_conv2_w.csv
- inception_4a_1x1_bn_b.csv
- inception_4a_1x1_bn_m.csv
- inception_4a_1x1_bn_v.csv
- inception_4a_1x1_bn_w.csv
- inception_4a_1x1_conv_b.csv
- inception_4a_1x1_conv_w.csv
- inception_4a_3x3_bn1_b.csv
- inception_4a_3x3_bn1_m.csv
- inception_4a_3x3_bn1_v.csv
- inception_4a_3x3_bn1_w.csv
- inception_4a_3x3_bn2_b.csv
- inception_4a_3x3_bn2_m.csv
- inception_4a_3x3_bn2_v.csv
- inception_4a_3x3_bn2_w.csv
- inception_4a_3x3_conv1_b.csv
- inception_4a_3x3_conv1_w.csv
- inception_4a_3x3_conv2_b.csv
- inception_4a_3x3_conv2_w.csv
- inception_4a_5x5_bn1_b.csv
- inception_4a_5x5_bn1_m.csv
- inception_4a_5x5_bn1_v.csv
- inception_4a_5x5_bn1_w.csv
- inception_4a_5x5_bn2_b.csv
- inception_4a_5x5_bn2_m.csv
- inception_4a_5x5_bn2_v.csv
- inception_4a_5x5_bn2_w.csv
- inception_4a_5x5_conv1_b.csv
- inception_4a_5x5_conv1_w.csv
- inception_4a_5x5_conv2_b.csv
- inception_4a_5x5_conv2_w.csv
- inception_4a_pool_bn_b.csv
- inception_4a_pool_bn_m.csv
- inception_4a_pool_bn_v.csv
- inception_4a_pool_bn_w.csv
- inception_4a_pool_conv_b.csv
- inception_4a_pool_conv_w.csv
- inception_4e_3x3_bn1_b.csv
- inception_4e_3x3_bn1_m.csv
- inception_4e_3x3_bn1_v.csv
- inception_4e_3x3_bn1_w.csv
- inception_4e_3x3_bn2_b.csv
- inception_4e_3x3_bn2_m.csv
- inception_4e_3x3_bn2_v.csv
- inception_4e_3x3_bn2_w.csv
- inception_4e_3x3_conv1_b.csv
- inception_4e_3x3_conv1_w.csv
- inception_4e_3x3_conv2_b.csv
- inception_4e_3x3_conv2_w.csv
- inception_4e_5x5_bn1_b.csv
- inception_4e_5x5_bn1_m.csv
- inception_4e_5x5_bn1_v.csv
- inception_4e_5x5_bn1_w.csv
- inception_4e_5x5_bn2_b.csv
- inception_4e_5x5_bn2_m.csv
- inception_4e_5x5_bn2_v.csv
- inception_4e_5x5_bn2_w.csv
- inception_4e_5x5_conv1_b.csv
- inception_4e_5x5_conv1_w.csv
- inception_4e_5x5_conv2_b.csv
- inception_4e_5x5_conv2_w.csv
- inception_5a_1x1_bn_b.csv
- inception_5a_1x1_bn_m.csv
- inception_5a_1x1_bn_v.csv
- inception_5a_1x1_bn_w.csv
- inception_5a_1x1_conv_b.csv
- inception_5a_1x1_conv_w.csv
- inception_5a_3x3_bn1_b.csv
- inception_5a_3x3_bn1_m.csv
- inception_5a_3x3_bn1_v.csv
- inception_5a_3x3_bn1_w.csv
- inception_5a_3x3_bn2_b.csv
- inception_5a_3x3_bn2_m.csv
- inception_5a_3x3_bn2_v.csv
- inception_5a_3x3_bn2_w.csv
- inception_5a_3x3_conv1_b.csv
- inception_5a_3x3_conv1_w.csv
- inception_5a_3x3_conv2_b.csv
- inception_5a_3x3_conv2_m.csv
- inception_5a_3x3_conv2_v.csv
- inception_5a_3x3_conv2_w.csv
- inception_5a_pool_bn_b.csv
- inception_5a_pool_bn_m.csv
- inception_5a_pool_bn_v.csv
- inception_5a_pool_bn_w.csv
- inception_5a_pool_conv_b.csv
- inception_5a_pool_conv_w.csv
- inception_5b_1x1_bn_b.csv
- inception_5b_1x1_bn_m.csv
- inception_5b_1x1_bn_v.csv
- inception_5b_1x1_bn_w.csv
- inception_5b_1x1_conv_b.csv
- inception_5b_1x1_conv_w.csv
- inception_5b_3x3_bn1_b.csv
- inception_5b_3x3_bn1_m.csv
- inception_5b_3x3_bn1_v.csv
- inception_5b_3x3_bn1_w.csv
- inception_5b_3x3_bn2_b.csv
- inception_5b_3x3_bn2_m.csv
- inception_5b_3x3_bn2_v.csv
- inception_5b_3x3_bn2_w.csv
- inception_5b_3x3_conv1_b.csv
- inception_5b_3x3_conv1_w.csv
- inception_5b_3x3_conv2_b.csv
- inception_5b_3x3_conv2_w.csv
- inception_5b_pool_bn_b.csv
- inception_5b_pool_bn_m.csv
- inception_5b_pool_bn_v.csv
- inception_5b_pool_bn_w.csv
- inception_5b_pool_conv_b.csv
- inception_5b_pool_conv_w.csv
images
- camp-nou.jpg
- cat.jpg
- circle_abstract.png
- claude-monet.jpg
- content_plus_style.png
- content.jpeg
- content300.jpg
- drop-of-water.jpg
- gram.png
- hidden_layers.png
- louvre_generated.png
- louvre_small.jpg
- louvre.jpg
- monet_800600.jpg
- monet.jpg
- NST_GM.png
- NST_LOSS.png
- pasargad_kashi.png
- persian_cat_content.jpg
- persian_cat.jpg
- perspolis_vangogh.png
- reshape_loss.png
- result.png
- sandstone.jpg
- stone_style.jpg
- style300.jpg
- nst_utils.py
output
- 0.png
- 20.png
- 40.png
- 60.png
- 80.png
- 100.png
- 120.png
- 140.png
- 160.png
- 180.png
- generated_image.jpg
pretrained-model
- Face Verification and Binary Classification _CORRECTION_ _ Coursera.pdf
Quiz
- Special applications_ Face recognition _ Neural style transfer _ Coursera.mhtml
- Style Cost _CORRECTION_ _ Coursera.pdf
- Triplet Loss _CORRECTION_ _ Coursera.pdf
Week1
- 01-Why sequence models.mp4
- 02-Notation.mp4
- 03-Recurrent Neural Network Model.mp4
- 04-Backpropagation through time.mp4
- 05-Different types of RNNs .mp4
- 06-Language model and sequence generation.mp4
- 07-Sampling novel sequences.mp4
- 08-Vanishing gradients with RNNs.mp4
- 10-Gated Recurrent Unit (GRU).mp4
- 11-Long Short Term Memory (LSTM).mp4
- 12-Bidirectional RNN.mp4
- 13-Deep RNNs.mp4
images
- initial_state.png
- LSTM_cell_backward_rev3a_5.png
- LSTM_cell_backward_rev3a_c2.png
- LSTM_figure4_v3a.png
- LSTM_rnn.png
- LSTM.png
- rnn_backward_overview_3a_1.png
- rnn_cell_backprop.png
- rnn_cell_backward_3a_4.png
- rnn_cell_backward_3a_c.png
- rnn_forward_sequence_figure3_v3a.png
- rnn_step_forward_figure2_v3a.png
- rnn_step_forward.png
- rnn.png
- RNN(1).png
images
- clip.png
- dino.jpg
- dinos3.png
- mangosaurus.jpeg
- rnn.png
- shakespeare.jpg
models
- model_shakespeare_kiank_350_epoch.h5
- model_shakespeare_kiank.h5
data
- 30s_seq.mp3
- 30s_trained_model.mp3
- deepjazz_on_metheny...128_epochs.midi
- original_metheny.mid
- training_example.mp3
- data_utils.py
- grammar.py
images
- jazz.jpg
- model.png
- music_gen.png
- music_generation.png
- Improvise a Jazz Solo with an LSTM Network - v3.ipynb
- Improvise_a_Jazz_Solo_with_an_LSTM_Network_v3a.ipynb
- inference_code.py
- midi.py
- music_utils.py
output
- my_music.midi
- preprocess.py
Quiz
- Recurrent Neural Networks _ Coursera.mht
Week2
- 01-Word Representation.mp4
- 02-Using word embeddings.mp4
- 03-Properties of word embeddings.mp4
- 04-Embedding matrix.mp4
- 05-Learning word embeddings.mp4
- 06-Word2Vec.mp4
- 07-Negative Sampling.mp4
- 08-GloVe word vectors.mp4
- 09-Sentiment Classification.mp4
- 10-Debiasing word embeddings.mp4
data
- emojify_data.csv
- tes.csv
- tess.csv
- tesss.csv
- test_emoji.csv
- testing.csv
- train_emoji.csv
- emo_utils.py
- Emojify - v2.ipynb
- Emojify_v2a.ipynb
images
- data_set.png
- dataset_kiank.png
- emb_kiank.png
- embedding1.png
- emo_model.png
- emojifier-v2.png
- emojifierv1.png
- emojify_model.png
- emojiss.png
- image_1.png
- woebot.png
images
- 1-hot-vector.png
- cosine_sim.png
- equalize10.png
- lookup.png
- neutral.png
- neutralize_kiank.png
- neutralize.png
- Operations on word vectors - v2.ipynb
- Operations_on_word_vectors_v2a.ipynb
Quiz
- Natural Language Processing _ Word Embeddings _ Coursera.mht
Week3
- 01-Basic Models.mp4
- 02-Picking the most likely sentence.mp4
- 03-Beam Search.mp4
- 04-Refinements to Beam Search.mp4
- 05-Error analysis in beam search.mp4
- 06-Bleu Score (optional).mp4
- 07-Attention Model Intuition.mp4
- 08-Attention Model.mp4
- 09-Speech recognition.mp4
- 10-Trigger Word Detection.mp4
- 11-Conclusion and thank you.mp4
images
- attn_mechanism.png
- attn_model.png
- date_attention.png
- date_attention2.png
- poorly_trained_model.png
- table.png
models
- model_kiank.h5
- model_sisi_oklm.h5
- model.h5
- oklmkiank.h5
- Neural machine translation with attention - v4.ipynb
- Neural_machine_translation_with_attention_v4a.ipynb
- nmt_utils.py
audio_examples
- chime.wav
- example_train.wav
- insert_reference.wav
- my_audio.wav
- train_reference.wav
- chime_output.wav
images
- label_diagram.png
- model.png
- ones_reference.png
- sound.png
- spectrogram.png
- train_label.png
- train_reference.png
- insert_test.wav
activates
- 1_act2.wav
- 1_act3.wav
- 1.wav
- 2_act2.wav
- 2_act3.wav
- 2.wav
- 3_act2.wav
- 3_act3.wav
- 3.wav
- 4_act2.wav
backgrounds
- 1.wav
- 2.wav
dev
- 1.wav
- 2.wav
negatives
- 1_0.wav
- 1.wav
- 2_1.wav
- 2.wav
- 3_2.wav
- 3.wav
- 4_0.wav
- 4.wav
- 5_1.wav
- 5.wav
- td_utils.py
- train.wav
- Trigger word detection - v1.ipynb
XY_dev
- Y_dev.npy
XY_train
- Y.npy
- Bleu Score _CORRECTION_ _ Coursera.pdf
- Corrections _ Coursera.pdf
- Coursera Deep Learning Module 5 Week 3 Notes _ XAI - eXplainable AI.mht
- Instructions if you are unable to open your notebook _ Coursera.pdf
Quiz
- Sequence models _ Attention mechanism _ Coursera.mht
- Workera_s Standardized Tests for AI Skills _ Coursera.pdf

Logistic Regression with a Neural Network mindset v5.ipynb

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Deep Learning Specialization - Andrew

Logistic Regression with a Neural Network mindset v5.ipynb

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