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 1. Introduction

  1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..srt

  3. BONUS 10 advantages of TensorFlow.html

 10. Dataset Preprocessing with TensorFlow Transform (TFT)

  1. Project Setup.srt

  1.2 pollution_small.csv

  2. Initial dataset preprocessing.srt

  3. Dataset metadata.srt

  4. Preprocessing function.srt

  5. Dataset preprocessing pipeline.srt

 11. Fashion API with Flask and TensorFlow 2.0

  1. Project Setup.srt

  2. Importing project dependencies.mp4

  3. Loading a pre-trained model.mp4

  4. Defining the Flask application.mp4

  5. Creating classify function.mp4

  6. Starting the Flask application.mp4

  7. Sending API requests over internet to the model.mp4

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 12. Image Classification API with TensorFlow Serving

  1. What is the TensorFlow Serving.srt

  10. Sending the POST request to a specific model.srt

  2. TensorFlow Serving architecture.srt

  3. Project setup.srt

  4. Dataset preprocessing.mp4

  5. Defining, training and evaluating a model.mp4

  6. Saving the model for production.mp4

  7. Serving the TensorFlow 2.0 Model.mp4

  8. Creating a JSON object.mp4

  9. Sending the first POST request to the model.mp4

 13. TensorFlow Lite Prepare a model for a mobile device

  1. What is the TensorFlow Lite.srt

  2. Project setup.mp4

  2.1 Google Colab TensorFlow Lite.html

  3. Dataset preprocessing.srt

  4. Building a model.srt

  5. Training, evaluating the model.srt

  6. Saving the model.srt

  7. TensorFlow Lite Converter.srt

  8. Converting the model to a TensorFlow Lite model.srt

  9. Saving the converted model.srt

 14. Distributed Training with TensorFlow 2.0

  1. What is the Distributed Training.srt

  2. Project Setup.srt

  3. Dataset preprocessing.mp4

  4. Defining a non-distributed model (normal CNN model).mp4

  5. Setting up a distributed strategy.mp4

  6. Defining a distributed model.mp4

  7. Final evaluation - Speed test normal model vs distributed model.mp4

 15. Annex 1 - Artificial Neural Networks Theory

  1. Plan of Attack.srt

  2. The Neuron.srt

  3. The Activation Function.srt

  4. How do Neural Networks Work.srt

  5. How do Neural Networks Learn.srt

  6. Gradient Descent.srt

  7. Stochastic Gradient Descent.srt

  8. Backpropagation.srt

 16. Annex 2 - Convolutional Neural Networks Theory

  1. Plan of Attack.srt

  2. What are Convolutional Neural Networks.srt

  3. Step 1 - Convolution.srt

  4. Step 1 Bis - ReLU Layer.srt

  5. Step 2 - Max Pooling.srt

  6. Step 3 - Flattening.srt

  7. Step 4 - Full Connection.srt

  8. Summary.srt

  9. Softmax & Cross-Entropy.srt

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 17. Annex 3 - Recurrent Neural Networks Theory

  1. Plan of Attack.srt

  2. What are Recurrent Neural Networks.srt

  3. Vanishing Gradient.srt

  4. LSTMs.srt

  5. LSTM Practical Intuition.srt

  6. LSTM Variations.srt

 18. Bonus Lectures

  2. YOUR SPECIAL BONUS.html

 2. TensorFlow 2.0 Basics

  1. From TensorFlow 1.x to TensorFlow 2.0.srt

  2. Constants, Variables, Tensors.mp4

  3. Operations with Tensors.mp4

  4. Strings.mp4

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 3. Artificial Neural Networks

  1. Project Setup.srt

  2. Data Preprocessing.mp4

  3. Building the Artificial Neural Network.mp4

  4. Training the Artificial Neural Network.mp4

  5. Evaluating the Artificial Neural Network.mp4

  6. Artificial Neural Network Quiz.html

  8. HOMEWORK SOLUTION Artificial Neural Networks.html

 4. Convolutional Neural Networks

  1. Project Setup & Data Preprocessing.srt

  2. Building the Convolutional Neural Network.mp4

  3. Training and Evaluating the Convolutional Neural Network.mp4

  4. Convolutional Neural Networks Quiz.html

  6. HOMEWORK SOLUTION Convolutional Neural Networks.html

 5. Recurrent Neural Networks

  1. Project Setup & Data Preprocessing.srt

  2. Building the Recurrent Neural Network.mp4

  3. Training and Evaluating the Recurrent Neural Network.mp4

  4. Recurrent Neural Network Quiz.html

 6. Transfer Learning and Fine Tuning

  1. What is Transfer Learning.srt

  10. Transfer Learning.srt

  11. Evaluating Transfer Learning results.srt

  12. Fine Tuning model definition.srt

  13. Compiling the Fine Tuning model.srt

  14. Fine Tuning.srt

  15. Evaluating Fine Tuning results.srt

  2. Project Setup.mp4

  2.1 Google Colab Transfer Learning and Fine Tuning.html

  3. Dataset preprocessing.srt

  4. Loading the MobileNet V2 model.srt

  5. Freezing the pre-trained model.srt

  6. Adding a custom head to the pre-trained model.srt

  7. Defining the transfer learning model.srt

  8. Compiling the Transfer Learning model.srt

  9. Image Data Generators.srt

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 7. Deep Reinforcement Learning Theory

  1. What is Reinforcement Learning.srt

  2. The Bellman Equation.srt

  3. Markov Decision Process (MDP).srt

  4. Q-Learning Intuition.srt

  5. Temporal Difference.srt

  6. Deep Q-Learning Intuition - Step 1.srt

  7. Deep Q-Learning Intuition - Step 2.srt

  8. Experience Replay.srt

  9. Action Selection Policies.srt

 8. Deep Reinforcement Learning for Stock Market trading

  1. Project Setup.srt

  10. Defining the model.mp4

  11. Training loop - Step 1.mp4

  12. Training loop - Step 2.mp4

  2. AI Trader - Step 1.mp4

  3. AI Trader - Step 2.mp4

  4. AI Trader - Step 3.mp4

  5. AI Trader - Step 4.mp4

  6. AI Trader - Step 5.mp4

  7. Dataset Loader function.mp4

  8. State creator function.mp4

  8.1 Yahoo finance - APPLE stocks.html

  9. Loading the dataset.srt

 9. Data Validation with TensorFlow Data Validation (TFDV)

  1. Project Setup.srt

  1.2 Google Colab TFDV.html

  2. Loading the pollution dataset.srt

  3. Creating dataset Schema.srt

  4. Computing test set statistics.srt

  5. Anomaly detection with TensorFlow Data Validation.srt

  6. Preparing Schema for production.srt

  7. Saving the Schema.srt

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