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种子名称： [GigaCourse.Com] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence
文件类型： 视频
文件数目： 133个文件
文件大小： 6.83 GB
收录时间： 2023-6-15 13:28
已经下载： 3次
资源热度： 114
最近下载： 2024-11-20 06:58

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[GigaCourse.Com] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence.torrent

1. Welcome/1. Introduction.mp434.81MB

1. Welcome/2. Outline.mp473.67MB

1. Welcome/3. Where to get the code.mp462.91MB

10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp487.16MB

10. GANs (Generative Adversarial Networks)/2. GAN Code.mp478.3MB

11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp438.05MB

11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp440.11MB

11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp461.83MB

11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp456.27MB

11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp449.6MB

11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp437.7MB

11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp498.59MB

11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp443.33MB

11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp449.35MB

11. Deep Reinforcement Learning (Theory)/5. The Return.mp421.13MB

11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp443.56MB

11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp431.71MB

11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp442.74MB

11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp452.91MB

12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp426.04MB

12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.mp442.46MB

12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp450.97MB

12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.04MB

12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp425.98MB

12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp439.55MB

12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp468MB

12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp452.05MB

12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp452.51MB

12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp416.59MB

13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp427.78MB

13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4104.99MB

13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp442.59MB

13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp444.93MB

13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp443.54MB

13. Advanced Tensorflow Usage/6. Using the TPU.mp445.24MB

14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp438.68MB

14. Low-Level Tensorflow/2. Constants and Basic Computation.mp440.3MB

14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp456.05MB

14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp458.55MB

15. In-Depth Loss Functions/1. Mean Squared Error.mp433.77MB

15. In-Depth Loss Functions/2. Binary Cross Entropy.mp423.68MB

15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp431.7MB

16. In-Depth Gradient Descent/1. Gradient Descent.mp434.92MB

16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp422.97MB

16. In-Depth Gradient Descent/3. Momentum.mp434.25MB

16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp434.85MB

16. In-Depth Gradient Descent/5. Adam (pt 1).mp455.12MB

16. In-Depth Gradient Descent/6. Adam (pt 2).mp452.76MB

17. Extras/1. How to Choose Hyperparameters.mp437.92MB

17. Extras/2. Where Are The Exercises.mp425.98MB

18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4150.59MB

18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4180.9MB

18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4167.3MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp475.71MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp471.85MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp449.14MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp469.45MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp440.76MB

2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp453.84MB

2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp440.65MB

2. Google Colab/3. Uploading your own data to Google Colab.mp473.59MB

2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp438.93MB

2. Google Colab/5. How to Succeed in this Course.mp443.75MB

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp435.22MB

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4105.61MB

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.71MB

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.17MB

21. Appendix FAQ Finale/1. What is the Appendix.mp416.38MB

21. Appendix FAQ Finale/2. BONUS Lecture.mp437.79MB

3. Machine Learning and Neurons/1. What is Machine Learning.mp465.5MB

3. Machine Learning and Neurons/10. Why Keras.mp426.51MB

3. Machine Learning and Neurons/11. Suggestion Box.mp427.12MB

3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp459.8MB

3. Machine Learning and Neurons/3. Classification Notebook.mp454.54MB

3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp427.29MB

3. Machine Learning and Neurons/5. Regression Notebook.mp457.47MB

3. Machine Learning and Neurons/6. The Neuron.mp442.57MB

3. Machine Learning and Neurons/7. How does a model learn.mp447.95MB

3. Machine Learning and Neurons/8. Making Predictions.mp433.88MB

3. Machine Learning and Neurons/9. Saving and Loading a Model.mp429.73MB

4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp429.82MB

4. Feedforward Artificial Neural Networks/10. ANN for Regression.mp469.27MB

4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.mp468.52MB

4. Feedforward Artificial Neural Networks/3. Forward Propagation.mp446.7MB

4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.mp456.43MB

4. Feedforward Artificial Neural Networks/5. Activation Functions.mp480.54MB

4. Feedforward Artificial Neural Networks/6. Multiclass Classification.mp441.38MB

4. Feedforward Artificial Neural Networks/7. How to Represent Images.mp470.46MB

4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp450.92MB

4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp447.71MB

5. Convolutional Neural Networks/1. What is Convolution (part 1).mp479.77MB

5. Convolutional Neural Networks/10. Batch Normalization.mp421.11MB

5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp472.91MB

5. Convolutional Neural Networks/2. What is Convolution (part 2).mp422.27MB

5. Convolutional Neural Networks/3. What is Convolution (part 3).mp427.64MB

5. Convolutional Neural Networks/4. Convolution on Color Images.mp469.44MB

5. Convolutional Neural Networks/5. CNN Architecture.mp480.58MB

5. Convolutional Neural Networks/6. CNN Code Preparation.mp476.88MB

5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp442.79MB

5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp429.69MB

5. Convolutional Neural Networks/9. Data Augmentation.mp434.95MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp490.15MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp450.36MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp464.65MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4124.05MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp429.12MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp423.3MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp467.11MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp432.97MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp467.34MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp428.33MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp446.75MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp471.7MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp416.2MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp483MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp418.43MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp474.07MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp452.48MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp479.86MB

7. Natural Language Processing (NLP)/1. Embeddings.mp452.56MB

7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp457.04MB

7. Natural Language Processing (NLP)/3. Text Preprocessing.mp428.76MB

7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp450.68MB

7. Natural Language Processing (NLP)/5. CNNs for Text.mp440.4MB

7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp439.62MB

8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp468.66MB

8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp458.81MB

9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp455.13MB

9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp431.57MB

9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp436.56MB

9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp420.58MB

9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp466.52MB

9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp446.05MB