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Intermediate

week1.A new programming paradigm
- 1.Introduction: A conversation with Andrew Ng.mp4
  
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- 2.A primer in machine learning.mp4
  
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- 3.The ‘Hello World’ of neural networks.mp4
  
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- 4.Working through ‘Hello World’ in TensorFlow and Python.mp4
  
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- Before you begin_ TensorFlow 2.0 and this course _ Coursera.pdf
  
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- Exercise_1_House_Prices_Question.ipynb
  
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- From rules to data _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-20 17-59-43.png
  
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- Screenshot from 2021-08-20 17-59-51.png
  
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- Try it for yourself _ Coursera.pdf
  
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- Week 1 Resources _ Coursera.pdf
  
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- week1.tar.gz
  
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week2.Introduction to Computer Vision
- 1.A Conversation with Andrew Ng.mp4
  
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- 2.An Introduction to computer vision.mp4
  
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- 3.Writing code to load training data.mp4
  
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- 4.Coding a Computer Vision Neural Network.mp4
  
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- 5.Walk through a Notebook for computer vision.mp4
  
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- 6.Using Callbacks to control training.mp4
  
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- 7.Walk through a notebook with Callbacks.mp4
  
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- Exercise2_Question.ipynb
  
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- Exploring how to use data _ Coursera.pdf
  
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- Get hands-on with computer vision _ Coursera.pdf
  
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- notebook.tar.gz
  
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quiz
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- Screenshot from 2021-08-20 18-11-20.png
  
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- Screenshot from 2021-08-20 18-11-27.png
  
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- See how it done _ Coursera.pdf
  
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- See how to implement Callbacks _ Coursera.pdf
  
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- The structure of Fashion MNIST data _ Coursera.pdf
  
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- Week 2 Resources _ Coursera.pdf
  
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week3.Enhancing Vision with Convolutional Neural Networks
- 1.A conversation with Andrew Ng.mp4
  
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- 2.What are convolutions and pooling?.mp4
  
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- 3.Implementing convolutional layers.mp4
  
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- 4.Implementing pooling layers.mp4
  
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- 5.Improving the Fashion classifier with convolutions.mp4
  
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- 6.Walking through convolutions.mp4
  
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- Coding convolutions and pooling layers _ Coursera.pdf
  
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- Exercise_3_Question.ipynb
  
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- Experiment with filters and pools _ Coursera.pdf
  
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- Getting hands-on, your first ConvNet _ Coursera.pdf
  
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- Learn more about convolutions _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-20 18-21-06.png
  
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- Try it for yourself _ Coursera.pdf
  
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- Week 3 Resources _ Coursera.pdf
  
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- week3.tar.gz
  
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week4.Using Real-world Images
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Understanding ImageGenerator.mp4
  
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- 3.Defining a ConvNet to use complex images.mp4
  
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- 4.Training the ConvNet with fit_generator.mp4
  
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- 5.Walking through developing a ConvNet.mp4
  
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- 6.Walking through training the ConvNet with fit_generator.mp4
  
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- 7.Adding automatic validation to test accuracy.mp4
  
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- 8.Exploring the impact of compressing images.mp4
  
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- Designing the neural network _ Coursera.pdf
  
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- Exercise_4_Question.ipynb
  
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- Experiment with the horse or human classifier _ Coursera.pdf
  
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- Explore an impactful, real-world solution _ Coursera.pdf
  
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- Exploring the solution _ Coursera.pdf
  
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- Get hands-on and use validation _ Coursera.pdf
  
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- Get Hands-on with compacted images _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-20 22-22-16.png
  
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- Screenshot from 2021-08-20 22-22-26.png
  
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- Train the ConvNet with ImageGenerator _ Coursera.pdf
  
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- Training the neural network _ Coursera.pdf
  
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- Week 4 Resources _ Coursera.pdf
  
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- week4.tar.gz
  
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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Introduction, A conversation with Andrew Ng.mp4
  
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week1.Exploring a Larger Dataset
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Training with the cats vs. dogs dataset.mp4
  
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- 3.Working through the notebook.mp4
  
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- 4.Fixing through cropping.mp4
  
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- 5.Visualizing the effect of the convolutions.mp4
  
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- 6.Looking at accuracy and loss.mp4
  
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- 7.Week 1 Wrap up.mp4
  
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- Exercise_5_Question.ipynb
  
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- Looking at the notebook _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-20 23-09-13.png
  
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- Screenshot from 2021-08-20 23-09-25.png
  
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- The cats vs dogs dataset _ Coursera.pdf
  
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- week1.tar.gz
  
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- What have we seen so far_ _ Coursera.pdf
  
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- What you'll see next _ Coursera.pdf
  
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week2.Augmentation: A technique to avoid overfitting
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Introducing augmentation.mp4
  
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- 3.Coding augmentation with ImageDataGenerator.mp4
  
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- 4.Demonstrating overfitting in cats vs. dogs.mp4
  
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- 5.Adding augmentation to cats vs. dogs.mp4
  
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- 6.Exploring augmentation with horses vs. humans.mp4
  
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- 7.Week 2 Wrap up.mp4
  
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- Exercise_6_Question.ipynb
  
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- Image Augmentation _ Coursera.pdf
  
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- Looking at the notebook _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-20 23-19-33.png
  
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- Screenshot from 2021-08-20 23-19-39.png
  
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- Start Coding... _ Coursera.pdf
  
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- The impact of augmentation on Cats vs. Dogs _ Coursera.pdf
  
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- Try it for yourself! _ Coursera.pdf
  
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- week2.tar.gz
  
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- What have we seen so far_ _ Coursera.pdf
  
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week3.Transfer Learning
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Understanding transfer learning: the concepts.mp4
  
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- 3.Coding transfer learning from the inception mode.mp4
  
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- 4.Coding your own model with transferred features.mp4
  
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- 5.Exploring dropouts.mp4
  
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- 6.Exploring Transfer Learning with Inception.mp4
  
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- 7.Week 3 Wrap up.mp4
  
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- Adding your DNN _ Coursera.pdf
  
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- Applying Transfer Learning to Cats v Dogs _ Coursera.pdf
  
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- Exercise_7_Question.ipynb
  
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quiz
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- Screenshot from 2021-08-20 23-34-32.png
  
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- Screenshot from 2021-08-20 23-34-39.png
  
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- Start coding! _ Coursera.pdf
  
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- Using dropouts! _ Coursera.pdf
  
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- week3.tar.gz
  
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- What have we seen so far_ _ Coursera.pdf
  
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week4.Multiclass Classifications
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Moving from binary to multi-class classification.mp4
  
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- 3.Explore multi-class with Rock Paper Scissors dataset.mp4
  
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- 4.Train a classifier with Rock Paper Scissors.mp4
  
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- 5.Test the Rock Paper Scissors classifier.mp4
  
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- 6.A conversation with Andrew Ng.mp4
  
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- Check out the code! _ Coursera.pdf
  
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- Exercise_8_Question.ipynb
  
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- Introducing the Rock-Paper-Scissors dataset _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-20 23-41-12.png
  
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- Screenshot from 2021-08-20 23-41-17.png
  
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- Try testing the classifier _ Coursera.pdf
  
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- week4.tar.gz
  
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- What have we seen so far_ _ Coursera.pdf
  
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- Wrap up _ Coursera.pdf
  
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week1.Sentiment in text
- 1.Introduction.mp4
  
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- 2.Word based encodings.mp4
  
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- 3.Using APIs.mp4
  
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- 4.Notebook for lesson 1.mp4
  
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- 5.Text to sequence.mp4
  
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- 6.Looking more at the Tokenizer.mp4
  
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- 7.Padding.mp4
  
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- 8.Notebook for lesson 2.mp4
  
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- 9.Sarcasm, really?.mp4
  
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- 10.Working with the Tokenizer.mp4
  
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- 11.Notebook for lesson 3.mp4
  
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- 12.Week 1 Wrap up.mp4
  
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- Check out the code! _ Coursera.pdf
  
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- Check out the code! _ Coursera1.pdf
  
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- Check out the code! _ Coursera2.pdf
  
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- Course_3_Week_1_Exercise_answer.ipynb
  
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- Course_3_Week_1_Exercise_question.ipynb
  
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- News headlines dataset for sarcasm detection _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-21 09-31-21.png
  
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- Screenshot from 2021-08-21 09-31-27.png
  
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week2.Word Embeddings
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.The IMBD dataset.mp4
  
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- 4.Looking into the details.mp4
  
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- 5.How can we use vectors?.mp4
  
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- 6.More into the details.mp4
  
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- 7.Notebook for lesson 1.mp4
  
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- 8.Remember the sarcasm dataset?.mp4
  
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- 9.Building a classifier for the sarcasm dataset.mp4
  
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- 10.Let’s talk about the loss function.mp4
  
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- 11.Pre-tokenized datasets.mp4
  
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- 12.Diving into the code (part 1).mp4
  
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- 13.Diving into the code (part 2).mp4
  
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- 14.Notebook for lesson 3.mp4
  
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- Check out the code! _ Coursera.pdf
  
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- Check out the code! _ Coursera1.pdf
  
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- Check out the code! _ Coursera2.pdf
  
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- Course_3_Week_2_Exercise_Answer.ipynb
  
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- Course_3_Week_2_Exercise_Question.ipynb
  
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- IMDB reviews dataset _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-21 09-40-37.png
  
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- Screenshot from 2021-08-21 09-40-42.png
  
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- Subwords text encoder _ Coursera.pdf
  
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- TensorFlow datasets _ Coursera.pdf
  
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- Week 2 Wrap up _ Coursera.pdf
  
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week3.Sequence models
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.LSTMs.mp4
  
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- 4.Implementing LSTMs in code.mp4
  
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- 5.Accuracy and loss.mp4
  
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- 6.A word from Laurence.mp4
  
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- 7.Looking into the code.mp4
  
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- 8.Using a convolutional network.mp4
  
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- 9.Going back to the IMDB dataset.mp4
  
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- 10.Tips from Laurence.mp4
  
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- Check out the code! _ Coursera.pdf
  
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- Check out the code! _ Coursera1.pdf
  
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- Check out the code! _ Coursera2.pdf
  
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- Exploring different sequence models _ Coursera.pdf
  
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- Link to Andrew's sequence modeling course _ Coursera.pdf
  
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- More info on LSTMs _ Coursera.pdf
  
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- NLP_Course_Week_3_Exercise_Answer.ipynb
  
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- NLP_Course_Week_3_Exercise_Question.ipynb
  
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quiz
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- Screenshot from 2021-08-21 09-47-02.png
  
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- Screenshot from 2021-08-21 09-47-08.png
  
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- Week 3 Wrap up _ Coursera.pdf
  
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week4.Sequence models and literature
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.Looking into the code.mp4
  
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- 4.Training the data.mp4
  
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- 5.More on training the data.mp4
  
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- 6.Notebook for lesson 1.mp4
  
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- 7.Finding what the next word should be.mp4
  
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- 8.Example.mp4
  
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- 9.Predicting a word.mp4
  
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- 10.Poetry!.mp4
  
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- 11.Looking into the code.mp4
  
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- 12.Laurence the poet!.mp4
  
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- 13.Your next task.mp4
  
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- 14.A conversation with Andrew Ng.mp4
  
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- Check out the code! _ Coursera.pdf
  
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- Check out the code! _ Coursera1.pdf
  
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- Link to generating text using a character-based RNN _ Coursera.pdf
  
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- link to Laurence's poetry _ Coursera.pdf
  
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- NLP_Week4_Exercise_Shakespeare_Answer.ipynb
  
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- NLP_Week4_Exercise_Shakespeare_Question.ipynb
  
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quiz
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- Screenshot from 2021-08-21 10-03-19.png
  
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- Screenshot from 2021-08-21 10-03-24.png
  
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- Wrap up _ Coursera.pdf
  
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week1.Sequences and Prediction
- 1.Time series examples.mp4
  
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- 2.Machine learning applied to time series.mp4
  
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- 3.Common patterns in time series.mp4
  
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- 4.Introduction to time series.mp4
  
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- 5.Train, validation and test sets.mp4
  
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- 6.Metrics for evaluating performance.mp4
  
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- 7.Moving average and differencing.mp4
  
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- 8.Trailing versus centered windows.mp4
  
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- 9.Forecasting.mp4
  
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- Forecasting notebook _ Coursera.pdf
  
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- Introduction to time series notebook _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-21 10-10-06.png
  
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- Screenshot from 2021-08-21 10-10-17.png
  
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- Screenshot from 2021-08-21 10-10-24.png
  
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- Week 1 Wrap up _ Coursera.pdf
  
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- Week_1_Exercise_Answer.ipynb
  
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- Week_1_Exercise_Question.ipynb
  
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week2.Deep Neural Networks for Time Series
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Preparing features and labels.mp4
  
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- 3.Preparing features and labels.mp4
  
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- 4.Feeding windowed dataset into neural network.mp4
  
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- 5.Single layer neural network.mp4
  
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- 6.Machine learning on time windows.mp4
  
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- 7.Prediction.mp4
  
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- 8.More on single layer neural network.mp4
  
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- 9.Deep neural network training, tuning and prediction.mp4
  
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- 10.Deep neural network.mp4
  
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- Deep neural network notebook _ Coursera.pdf
  
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- Preparing features and labels notebook _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-21 10-19-00.png
  
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- Screenshot from 2021-08-21 10-19-05.png
  
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- S+P_Week_2_Exercise_Answer.ipynb
  
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- S+P_Week_2_Exercise_Question.ipynb
  
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- Sequence bias _ Coursera.pdf
  
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- Single layer neural network notebook _ Coursera.pdf
  
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- Week 2 Wrap up _ Coursera.pdf
  
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week3.Recurrent Neural Networks for Time Series
- 1.Week 3 - A conversation with Andrew Ng.mp4
  
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- 2.Conceptual overview.mp4
  
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- 3.Shape of the inputs to the RNN.mp4
  
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- 4.Outputting a sequence.mp4
  
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- 5.Lambda layers.mp4
  
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- 6.Adjusting the learning rate dynamically.mp4
  
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- 7.RNN.mp4
  
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- 8.LSTM.mp4
  
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- 9.Coding LSTMs.mp4
  
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- 10.More on LSTM.mp4
  
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- Link to the LSTM lesson _ Coursera.pdf
  
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- LSTM notebook _ Coursera.pdf
  
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- More info on Huber loss _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-21 10-25-02.png
  
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- Screenshot from 2021-08-21 10-25-08.png
  
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- RNN notebook _ Coursera.pdf
  
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- S+P_Week_3_Exercise_Answer.ipynb
  
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- S+P_Week_3_Exercise_Question.ipynb
  
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- Week 3 Wrap up _ Coursera.pdf
  
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week4.Real-world time series data
- (Optional) Opportunity to Mentor Other Learners _ Coursera.pdf
  
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- 1.Week 4 - A conversation with Andrew Ng.mp4
  
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- 2.Convolutions.mp4
  
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- 3.Bi-directional LSTMs.mp4
  
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- 4.LSTM.mp4
  
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- 5.Real data - sunspots.mp4
  
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- 6.Train and tune the model.mp4
  
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- 7.Prediction.mp4
  
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- 8.Sunspots.mp4
  
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- 9.Combining our tools for analysis.mp4
  
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- 10.Congratulations.mp4
  
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- 11.Specialization wrap up - A conversation with Andrew Ng.mp4
  
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- Convolutional neural networks course _ Coursera.pdf
  
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- LSTM notebook _ Coursera.pdf
  
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- More on batch sizing _ Coursera.pdf
  
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quiz
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- Screenshot from 2021-08-21 10-32-06.png
  
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- Screenshot from 2021-08-21 10-32-12.png
  
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- S+P_Week_4_Exercise_Answer.ipynb
  
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- S+P_Week_4_Exercise_Question.ipynb
  
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- Sunspots notebook _ Coursera.pdf
  
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- What next_ _ Coursera.pdf
  
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- Wrap up _ Coursera.pdf
  
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