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

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种子名称： [DesireCourse.Net] Udemy - A Complete Guide on TensorFlow 2.0 using Keras API
文件类型：视频
文件数目： 120个文件
文件大小： 5.12 GB
收录时间： 2020-6-20 14:05
已经下载： 3次
资源热度： 237
最近下载： 2024-12-22 03:28

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1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4146.29MB

10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.mp410.06MB

10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.mp434.99MB

10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.mp420.93MB

10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.mp421.12MB

10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.mp473.96MB

11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.mp436.74MB

11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.mp411.91MB

11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.mp420.49MB

11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.mp412.38MB

11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.mp453.14MB

11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.mp427.62MB

11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.mp435.02MB

12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.mp424.47MB

12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.mp49.64MB

12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.mp419.51MB

12. Image Classification API with TensorFlow Serving/3. Project setup.mp425.53MB

12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.mp423.72MB

12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.mp423.33MB

12. Image Classification API with TensorFlow Serving/6. Saving the model for production.mp425.43MB

12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.mp427.91MB

12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.mp423.59MB

12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.mp427.32MB

13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.mp413.97MB

13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.mp48.05MB

13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.mp428.77MB

13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.mp414.84MB

13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.mp415.2MB

13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.mp49.41MB

13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.mp46.29MB

13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.mp44.93MB

13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.mp48.68MB

14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.mp411.09MB

14. Distributed Training with TensorFlow 2.0/2. Project Setup.mp49.08MB

14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.mp425.59MB

14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).mp414.05MB

14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.mp47.4MB

14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.mp412.49MB

14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.mp428.42MB

15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.mp411.83MB

15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp498.69MB

15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.mp445.33MB

15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.mp481.81MB

15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4112.16MB

15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.mp460.57MB

15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.mp467.24MB

15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.mp443.13MB

16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.mp415.79MB

16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4107.87MB

16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp497.84MB

16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.mp453.37MB

16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4140.21MB

16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.mp47.92MB

16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4194.15MB

16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.mp430.32MB

16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4117.84MB

17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.mp410.48MB

17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.mp4120.95MB

17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4111MB

17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4136.42MB

17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4187.41MB

17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.mp420.13MB

2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4114.8MB

2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.mp471.34MB

2. TensorFlow 2.0 Basics/3. Operations with Tensors.mp449.26MB

2. TensorFlow 2.0 Basics/4. Strings.mp440.24MB

3. Artificial Neural Networks/1. Project Setup.mp459.26MB

3. Artificial Neural Networks/2. Data Preprocessing.mp461.77MB

3. Artificial Neural Networks/3. Building the Artificial Neural Network.mp460.44MB

3. Artificial Neural Networks/4. Training the Artificial Neural Network.mp448.52MB

3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.mp431.45MB

4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.mp447.37MB

4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.mp488.18MB

4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.mp458.24MB

5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.mp446.44MB

5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.mp440.03MB

5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.mp448.87MB

6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.mp446.49MB

6. Transfer Learning and Fine Tuning/10. Transfer Learning.mp416.83MB

6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.mp49.37MB

6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.mp424.6MB

6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.mp46.41MB

6. Transfer Learning and Fine Tuning/14. Fine Tuning.mp410.18MB

6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.mp49.01MB

6. Transfer Learning and Fine Tuning/2. Project Setup.mp449.38MB

6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.mp431.84MB

6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.mp417.84MB

6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.mp46.07MB

6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.mp419.69MB

6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.mp413.2MB

6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.mp412.59MB

6. Transfer Learning and Fine Tuning/9. Image Data Generators.mp432.56MB

7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.mp468.54MB

7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp495.06MB

7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp494.29MB

7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.mp479.08MB

7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp497.1MB

7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp499.92MB

7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.mp443.07MB

7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4114.74MB

7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4136.84MB

8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.mp411.89MB

8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.mp411.87MB

8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.mp428.12MB

8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.mp454.18MB

8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.mp427.2MB

8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.mp411.9MB

8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.mp415.84MB

8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.mp415.92MB

8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.mp433.2MB

8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.mp438.89MB

8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.mp432.32MB

8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.mp410.03MB

9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.mp422.28MB

9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.mp424.84MB

9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.mp424.09MB

9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.mp42.45MB

9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.mp423.96MB

9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.mp419.71MB

9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.mp48.1MB