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文档列表

16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4 203.6 MB
17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4 196.5 MB
1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4 153.4 MB
16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4 147.0 MB
7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4 143.5 MB
17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4 143.1 MB
17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.mp4 126.8 MB
16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4 123.6 MB
2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4 120.4 MB
7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4 120.3 MB
15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4 117.6 MB
17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4 116.4 MB
16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4 113.1 MB
7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp4 104.8 MB
15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp4 103.5 MB
16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp4 102.6 MB
7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp4 101.8 MB
7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp4 99.7 MB
7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp4 98.9 MB
4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.mp4 92.5 MB
15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.mp4 85.8 MB
7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.mp4 82.9 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.mp4 77.6 MB
2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.mp4 74.8 MB
7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.mp4 71.9 MB
15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.mp4 70.5 MB
3. Artificial Neural Networks/2. Data Preprocessing.mp4 64.8 MB
15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.mp4 63.5 MB
3. Artificial Neural Networks/3. Building the Artificial Neural Network.mp4 63.4 MB
3. Artificial Neural Networks/1. Project Setup.mp4 62.1 MB
4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.mp4 61.1 MB
8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.mp4 56.8 MB
16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.mp4 56.0 MB
11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.mp4 55.7 MB
6. Transfer Learning and Fine Tuning/2. Project Setup.mp4 51.8 MB
2. TensorFlow 2.0 Basics/3. Operations with Tensors.mp4 51.6 MB
5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.mp4 51.3 MB
3. Artificial Neural Networks/4. Training the Artificial Neural Network.mp4 50.9 MB
4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.mp4 49.7 MB
6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.mp4 48.8 MB
5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.mp4 48.7 MB
15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.mp4 47.5 MB
15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.mp4 45.2 MB
7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.mp4 45.2 MB
2. TensorFlow 2.0 Basics/4. Strings.mp4 42.2 MB
5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.mp4 42.0 MB
8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.mp4 40.8 MB
11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.mp4 38.5 MB
11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.mp4 36.7 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.mp4 36.7 MB
8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.mp4 34.8 MB
6. Transfer Learning and Fine Tuning/9. Image Data Generators.mp4 34.2 MB
8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.mp4 33.9 MB
6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.mp4 33.4 MB
3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.mp4 33.0 MB
16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.mp4 31.8 MB
13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.mp4 30.2 MB
14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.mp4 29.8 MB
8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.mp4 29.5 MB
12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.mp4 29.3 MB
11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.mp4 29.0 MB
12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.mp4 28.7 MB
8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.mp4 28.5 MB
14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.mp4 26.8 MB
12. Image Classification API with TensorFlow Serving/3. Project setup.mp4 26.8 MB
12. Image Classification API with TensorFlow Serving/6. Saving the model for production.mp4 26.7 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.mp4 26.0 MB
6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.mp4 25.8 MB
12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.mp4 25.6 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.mp4 25.3 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.mp4 25.1 MB
12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.mp4 24.9 MB
12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.mp4 24.7 MB
12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.mp4 24.5 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.mp4 23.4 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.mp4 22.1 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.mp4 22.0 MB
11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.mp4 21.5 MB
17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.mp4 21.1 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.mp4 20.7 MB
6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.mp4 20.7 MB
12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.mp4 20.5 MB
6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.mp4 18.7 MB
6. Transfer Learning and Fine Tuning/10. Transfer Learning.mp4 17.6 MB
8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.mp4 16.7 MB
8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.mp4 16.6 MB
16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.mp4 16.6 MB
13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.mp4 15.9 MB
13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.mp4 15.6 MB
14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).mp4 14.7 MB
13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.mp4 14.6 MB
6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.mp4 13.8 MB
6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.mp4 13.2 MB
14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.mp4 13.1 MB
11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.mp4 13.0 MB
11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.mp4 12.5 MB
8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.mp4 12.5 MB
8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.mp4 12.5 MB
8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.mp4 12.4 MB
15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.mp4 12.4 MB
14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.mp4 11.6 MB
17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.mp4 11.0 MB
6. Transfer Learning and Fine Tuning/14. Fine Tuning.mp4 10.7 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.mp4 10.5 MB
8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.mp4 10.5 MB
12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.mp4 10.1 MB
13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.mp4 9.9 MB
6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.mp4 9.8 MB
14. Distributed Training with TensorFlow 2.0/2. Project Setup.mp4 9.5 MB
6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.mp4 9.4 MB
13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.mp4 9.1 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.mp4 8.5 MB
13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.mp4 8.4 MB
16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.mp4 8.3 MB
14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.mp4 7.8 MB
6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.mp4 6.7 MB
13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.mp4 6.6 MB
6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.mp4 6.4 MB
13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.mp4 5.2 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.mp4 2.6 MB
11. Fashion API with Flask and TensorFlow 2.0/1.1 Flask API.zip 381.3 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.2 pollution_small.csv 74.5 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/1.2 pollution_small.csv 74.5 kB
7. Deep Reinforcement Learning Theory/2. The Bellman Equation.srt 32.0 kB
7. Deep Reinforcement Learning Theory/5. Temporal Difference.srt 29.5 kB
16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.srt 29.2 kB
17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.srt 28.8 kB
7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).srt 27.7 kB
1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..srt 27.0 kB
16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.srt 26.3 kB
15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.srt 25.6 kB
17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.srt 24.9 kB
7. Deep Reinforcement Learning Theory/9. Action Selection Policies.srt 24.6 kB
7. Deep Reinforcement Learning Theory/8. Experience Replay.srt 24.4 kB
16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.srt 23.9 kB
7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.srt 23.0 kB
16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.srt 22.6 kB
7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.srt 22.2 kB
17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.srt 21.5 kB
16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.srt 21.5 kB
17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.srt 21.0 kB
4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.srt 20.7 kB
15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.srt 19.6 kB
15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.srt 19.5 kB
7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.srt 18.7 kB
2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.srt 16.9 kB
3. Artificial Neural Networks/3. Building the Artificial Neural Network.srt 15.6 kB
15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.srt 14.4 kB
2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.srt 13.6 kB
15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.srt 12.6 kB
15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.srt 12.3 kB
4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.srt 11.6 kB
4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.srt 11.3 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.srt 10.9 kB
3. Artificial Neural Networks/2. Data Preprocessing.srt 10.8 kB
3. Artificial Neural Networks/4. Training the Artificial Neural Network.srt 10.6 kB
5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.srt 10.6 kB
5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.srt 10.4 kB
7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.srt 10.0 kB
16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.srt 9.9 kB
8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.srt 9.9 kB
8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.srt 9.9 kB
5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.srt 9.4 kB
2. TensorFlow 2.0 Basics/4. Strings.srt 9.0 kB
3. Artificial Neural Networks/1. Project Setup.srt 8.9 kB
2. TensorFlow 2.0 Basics/3. Operations with Tensors.srt 8.6 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.srt 8.6 kB
8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.srt 8.2 kB
8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.srt 7.9 kB
11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.srt 7.9 kB
12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.srt 7.7 kB
15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.srt 7.5 kB
12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.srt 7.3 kB
8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.srt 7.1 kB
3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.srt 7.0 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.srt 6.5 kB
6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.srt 6.5 kB
6. Transfer Learning and Fine Tuning/9. Image Data Generators.srt 6.5 kB
8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.srt 6.3 kB
6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.srt 6.2 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.srt 6.2 kB
16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.srt 6.2 kB
11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.srt 5.7 kB
14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.srt 5.6 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.srt 5.6 kB
16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.srt 5.5 kB
12. Image Classification API with TensorFlow Serving/6. Saving the model for production.srt 5.4 kB
13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.srt 5.3 kB
12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.srt 5.2 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.srt 5.1 kB
12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.srt 5.0 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.srt 4.9 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.srt 4.9 kB
12. Image Classification API with TensorFlow Serving/3. Project setup.srt 4.7 kB
17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.srt 4.7 kB
6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.srt 4.7 kB
6. Transfer Learning and Fine Tuning/2. Project Setup.srt 4.7 kB
12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.srt 4.6 kB
11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.srt 4.6 kB
14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.srt 4.5 kB
14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.srt 4.4 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.srt 4.4 kB
13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.srt 4.3 kB
6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.srt 4.3 kB
15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.srt 4.1 kB
11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.srt 4.0 kB
6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.srt 3.9 kB
14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).srt 3.8 kB
17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.srt 3.6 kB
6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.srt 3.6 kB
12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.srt 3.6 kB
8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.srt 3.5 kB
12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.srt 3.5 kB
13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.srt 3.3 kB
8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.srt 3.3 kB
6. Transfer Learning and Fine Tuning/10. Transfer Learning.srt 3.2 kB
8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.srt 3.0 kB
8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.srt 3.0 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.srt 2.8 kB
11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.srt 2.8 kB
16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.srt 2.7 kB
6. Transfer Learning and Fine Tuning/14. Fine Tuning.srt 2.7 kB
13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.srt 2.6 kB
11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.srt 2.6 kB
8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.srt 2.6 kB
13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.srt 2.5 kB
12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.srt 2.5 kB
6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.srt 2.4 kB
14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.srt 2.4 kB
18. Bonus Lectures/3. FREE LEARNING RESOURCES FOR YOU.html 2.4 kB
14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.srt 2.3 kB
13. TensorFlow Lite Prepare a model for a mobile device/10. What's next.html 2.2 kB
13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.srt 2.2 kB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/6. What's next.html 2.1 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/8. What's next.html 2.0 kB
14. Distributed Training with TensorFlow 2.0/2. Project Setup.srt 2.0 kB
13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.srt 2.0 kB
6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.srt 1.9 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.srt 1.9 kB
8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.srt 1.9 kB
13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.srt 1.8 kB
11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.srt 1.8 kB
6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.srt 1.8 kB
6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.srt 1.7 kB
6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.srt 1.5 kB
13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.srt 1.5 kB
1. Introduction/4. BONUS Learning Path.html 1.4 kB
18. Bonus Lectures/2. YOUR SPECIAL BONUS.html 1.2 kB
9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.srt 840 Bytes
18. Bonus Lectures/1. SPECIAL COVID-19 BONUS.html 722 Bytes
1. Introduction/3. BONUS 10 advantages of TensorFlow.html 613 Bytes
4. Convolutional Neural Networks/6. HOMEWORK SOLUTION Convolutional Neural Networks.html 573 Bytes
4. Convolutional Neural Networks/5. HOMEWORK Convolutional Neural Networks.html 500 Bytes
3. Artificial Neural Networks/7. HOMEWORK Artificial Neural Networks.html 493 Bytes
1. Introduction/2. Course Curriculum & Colab Toolkit.html 464 Bytes
3. Artificial Neural Networks/8. HOMEWORK SOLUTION Artificial Neural Networks.html 421 Bytes
10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.1 Google Colab TFT.html 134 Bytes
12. Image Classification API with TensorFlow Serving/3.1 Google Colab TensorFlow Serving.html 134 Bytes
13. TensorFlow Lite Prepare a model for a mobile device/2.1 Google Colab TensorFlow Lite.html 134 Bytes
14. Distributed Training with TensorFlow 2.0/2.1 Google Colab Distributed Training.html 134 Bytes
2. TensorFlow 2.0 Basics/1.1 Google Colab TensorFlow 1.x to TensorFlow 2.0.html 134 Bytes
3. Artificial Neural Networks/1.1 Google Colab ANN.html 134 Bytes
4. Convolutional Neural Networks/1.1 Google Colab CNN.html 134 Bytes
5. Recurrent Neural Networks/1.1 Google Colab RNN.html 134 Bytes
6. Transfer Learning and Fine Tuning/2.1 Google Colab Transfer Learning and Fine Tuning.html 134 Bytes
8. Deep Reinforcement Learning for Stock Market trading/1.1 Google Colab Deep-Q Trading Bot.html 134 Bytes
9. Data Validation with TensorFlow Data Validation (TFDV)/1.1 Google Colab TFDV.html 134 Bytes
0. Websites you may like/[FCS Forum].url 133 Bytes
0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
3. Artificial Neural Networks/6. Artificial Neural Network Quiz.html 123 Bytes
4. Convolutional Neural Networks/4. Convolutional Neural Networks Quiz.html 123 Bytes
5. Recurrent Neural Networks/4. Recurrent Neural Network Quiz.html 123 Bytes
6. Transfer Learning and Fine Tuning/16. Transfer Learning quiz.html 123 Bytes
0. Websites you may like/[CourseClub.ME].url 122 Bytes
8. Deep Reinforcement Learning for Stock Market trading/8.1 Yahoo finance - APPLE stocks.html 119 Bytes

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北川ゆい Akira COCOLO Saiko あいだももあさのくるみあまいれもんいしかわ愛里いとうしいなうさみ恭香うちだまひろかぐやひめかとりこのみかないかほくすのき琴美クミコグレースくらもとまい（葉月ありさ）さとみ中村あみしいな純菜しのざきさとみ（三沢亜也）牧本千幸（つかもと友希）眞木ありさデヴィテラパトリックドミニカともさかまいともさか愛なごみもえひなこ

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[FreeCourseSite.com] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API

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