[DesireCourse.Net] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API

Creation Time	May 7, 2024, 11:05 a.m.
Last Access Time	Feb. 15, 2025, 3 p.m.
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Keywords	A on Complete Keras DesireCourse - Udemy 0 2 using TensorFlow Net Guide API
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16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4.!qB 194.2 MB
17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4.!qB 187.4 MB
1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4.!qB 146.3 MB
16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4.!qB 137.2 MB
17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4.!qB 136.4 MB
7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4.!qB 134.7 MB
17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.mp4.!qB 120.9 MB
16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4.!qB 117.8 MB
2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4.!qB 114.8 MB
7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4.!qB 112.5 MB
15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4.!qB 112.2 MB
17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4.!qB 111.0 MB
16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4.!qB 107.9 MB
7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp4.!qB 99.5 MB
15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp4.!qB 98.7 MB
16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp4.!qB 97.8 MB
7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp4.!qB 97.1 MB
7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp4.!qB 95.1 MB
7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp4.!qB 94.3 MB
4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.mp4.!qB 88.2 MB
15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.mp4.!qB 81.8 MB
7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.mp4.!qB 79.1 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.mp4.!qB 74.0 MB
2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.mp4.!qB 71.3 MB
7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.mp4.!qB 68.5 MB
15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.mp4.!qB 67.2 MB
3. Artificial Neural Networks/2. Data Preprocessing.mp4.!qB 61.8 MB
15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.mp4.!qB 60.6 MB
3. Artificial Neural Networks/3. Building the Artificial Neural Network.mp4.!qB 60.4 MB
3. Artificial Neural Networks/1. Project Setup.mp4.!qB 59.3 MB
4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.mp4.!qB 58.2 MB
16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.mp4.!qB 53.4 MB
11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.mp4.!qB 53.1 MB
8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.mp4.!qB 53.0 MB
6. Transfer Learning and Fine Tuning/2. Project Setup.mp4.!qB 49.4 MB
2. TensorFlow 2.0 Basics/3. Operations with Tensors.mp4.!qB 49.3 MB
5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.mp4.!qB 48.9 MB
3. Artificial Neural Networks/4. Training the Artificial Neural Network.mp4.!qB 48.5 MB
4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.mp4.!qB 47.4 MB
6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.mp4.!qB 46.5 MB
5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.mp4.!qB 46.4 MB
15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.mp4.!qB 45.3 MB
15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.mp4.!qB 43.1 MB
7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.mp4.!qB 43.1 MB
2. TensorFlow 2.0 Basics/4. Strings.mp4.!qB 40.2 MB
5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.mp4.!qB 40.0 MB
11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.mp4.!qB 36.7 MB
11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.mp4.!qB 35.0 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.mp4 35.0 MB
8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.mp4.!qB 33.7 MB
8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.mp4.!qB 33.2 MB
6. Transfer Learning and Fine Tuning/9. Image Data Generators.mp4.!qB 32.6 MB
6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.mp4.!qB 31.8 MB
3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.mp4.!qB 31.4 MB
8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.mp4.!qB 30.8 MB
16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.mp4.!qB 30.3 MB
13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.mp4.!qB 28.8 MB
14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.mp4.!qB 28.4 MB
12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.mp4.!qB 27.9 MB
11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.mp4.!qB 27.6 MB
12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.mp4.!qB 27.3 MB
8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.mp4.!qB 26.8 MB
14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.mp4.!qB 25.6 MB
12. Image Classification API with TensorFlow Serving/3. Project setup.mp4.!qB 25.5 MB
12. Image Classification API with TensorFlow Serving/6. Saving the model for production.mp4.!qB 25.4 MB
8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.mp4.!qB 25.1 MB
6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.mp4.!qB 24.6 MB
12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.mp4.!qB 24.5 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.mp4.!qB 24.1 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.mp4.!qB 24.1 MB
12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.mp4.!qB 23.7 MB
12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.mp4.!qB 23.6 MB
12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.mp4.!qB 23.3 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.mp4 21.1 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.mp4 20.9 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.mp4.!qB 20.7 MB
11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.mp4.!qB 20.5 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.mp4.!qB 20.4 MB
17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.mp4.!qB 20.1 MB
6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.mp4.!qB 19.7 MB
12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.mp4.!qB 19.5 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.mp4.!qB 18.7 MB
6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.mp4.!qB 17.8 MB
6. Transfer Learning and Fine Tuning/10. Transfer Learning.mp4.!qB 16.8 MB
8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.mp4.!qB 15.9 MB
16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.mp4.!qB 15.8 MB
8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.mp4.!qB 15.7 MB
13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.mp4.!qB 15.2 MB
13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.mp4 14.8 MB
14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).mp4.!qB 14.0 MB
13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.mp4.!qB 14.0 MB
6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.mp4.!qB 13.2 MB
6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.mp4.!qB 12.6 MB
14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.mp4.!qB 12.5 MB
11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.mp4.!qB 12.4 MB
11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.mp4.!qB 11.9 MB
8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.mp4.!qB 11.9 MB
8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.mp4.!qB 11.9 MB
15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.mp4.!qB 11.8 MB
14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.mp4.!qB 11.1 MB
17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.mp4.!qB 10.5 MB
6. Transfer Learning and Fine Tuning/14. Fine Tuning.mp4.!qB 10.2 MB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.mp4 10.1 MB
12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.mp4.!qB 9.6 MB
8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.mp4.!qB 9.6 MB
8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.mp4.!qB 9.5 MB
13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.mp4.!qB 9.4 MB
6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.mp4.!qB 9.4 MB
14. Distributed Training with TensorFlow 2.0/2. Project Setup.mp4.!qB 9.1 MB
6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.mp4.!qB 9.0 MB
13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.mp4.!qB 8.7 MB
13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.mp4.!qB 8.1 MB
16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.mp4.!qB 7.9 MB
14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.mp4.!qB 7.4 MB
6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.mp4.!qB 6.4 MB
13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.mp4.!qB 6.3 MB
6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.mp4.!qB 6.1 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.mp4.!qB 6.0 MB
13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.mp4.!qB 4.9 MB
9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.mp4.!qB 2.5 MB
11. Fashion API with Flask and TensorFlow 2.0/1.1 Flask API.zip 372.4 KB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.2 pollution_small.csv 72.7 KB
7. Deep Reinforcement Learning Theory/2. The Bellman Equation.srt 31.3 KB
7. Deep Reinforcement Learning Theory/5. Temporal Difference.srt 28.8 KB
16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.srt 28.6 KB
17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.srt 28.2 KB
7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).srt 27.1 KB
1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..srt 26.4 KB
16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.srt 25.7 KB
15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.srt 25.0 KB
17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.srt 24.3 KB
16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.srt 23.3 KB
16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.srt 22.1 KB
7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.srt 21.7 KB
17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.srt 21.0 KB
16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.srt.!qB 21.0 KB
17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.srt 20.5 KB
4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.srt 20.2 KB
15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.srt 19.2 KB
15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.srt 19.0 KB
7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.srt 18.2 KB
2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.srt 16.5 KB
3. Artificial Neural Networks/3. Building the Artificial Neural Network.srt 15.2 KB
15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.srt 14.0 KB
2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.srt 13.3 KB
15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.srt 12.3 KB
15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.srt 12.0 KB
4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.srt 11.3 KB
4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.srt 11.0 KB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.srt 10.6 KB
3. Artificial Neural Networks/2. Data Preprocessing.srt 10.6 KB
3. Artificial Neural Networks/4. Training the Artificial Neural Network.srt 10.4 KB
5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.srt 10.3 KB
5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.srt 10.2 KB
7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.srt 9.8 KB
16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.srt 9.7 KB
5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.srt 9.2 KB
2. TensorFlow 2.0 Basics/4. Strings.srt 8.8 KB
3. Artificial Neural Networks/1. Project Setup.srt 8.7 KB
2. TensorFlow 2.0 Basics/3. Operations with Tensors.srt 8.4 KB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.srt 8.4 KB
11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.srt 7.7 KB
12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.srt 7.6 KB
15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.srt 7.3 KB
12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.srt 7.1 KB
3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.srt 6.8 KB
9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.srt 6.4 KB
6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.srt 6.3 KB
6. Transfer Learning and Fine Tuning/9. Image Data Generators.srt 6.3 KB
8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.srt 6.2 KB
6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.srt 6.1 KB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.srt 6.0 KB
16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.srt 6.0 KB
11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.srt 5.6 KB
14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.srt 5.5 KB
16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.srt 5.4 KB
12. Image Classification API with TensorFlow Serving/6. Saving the model for production.srt 5.3 KB
13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.srt 5.2 KB
12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.srt 5.1 KB
12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.srt 4.9 KB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.srt 4.8 KB
12. Image Classification API with TensorFlow Serving/3. Project setup.srt 4.6 KB
17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.srt 4.6 KB
6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.srt 4.6 KB
6. Transfer Learning and Fine Tuning/2. Project Setup.srt 4.5 KB
12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.srt 4.5 KB
11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.srt 4.5 KB
14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.srt 4.4 KB
14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.srt 4.3 KB
13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.srt 4.2 KB
6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.srt 4.2 KB
15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.srt 4.0 KB
11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.srt 3.9 KB
6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.srt 3.8 KB
14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).srt 3.7 KB
17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.srt 3.5 KB
6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.srt 3.5 KB
12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.srt 3.5 KB
8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.srt 3.4 KB
12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.srt 3.4 KB
13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.srt 3.2 KB
8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.srt 3.2 KB
6. Transfer Learning and Fine Tuning/10. Transfer Learning.srt 3.1 KB
8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.srt 2.9 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 2.7 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 2.6 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 2.5 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 2.4 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 2.3 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 2.1 KB
10. Dataset Preprocessing with TensorFlow Transform (TFT)/6. What's next.html 2.1 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 1.9 KB
6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.srt 1.9 KB
1. Introduction/4. BONUS Learning Path.html 1.8 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 1.7 KB
6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.srt 1.6 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 1.4 KB
18. Bonus Lectures/2. YOUR SPECIAL BONUS.html 1.1 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
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/[DesireCourse.Net].url 51 bytes
1. Introduction/[DesireCourse.Net].url 51 bytes
14. Distributed Training with TensorFlow 2.0/[DesireCourse.Net].url 51 bytes
0. Websites you may like/[CourseClub.Me].url 48 bytes
1. Introduction/[CourseClub.Me].url 48 bytes
14. Distributed Training with TensorFlow 2.0/[CourseClub.Me].url 48 bytes

[DesireCourse.Net] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API

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