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

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

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收录时间：2021-03-30
最近下载：2024-11-13

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文件列表

  • 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 55.9 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 51.2 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 45.1 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 34.1 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 28.6 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 21.9 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
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