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种子名称： [DesireCourse.Net] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence
文件类型： 视频
文件数目： 125个文件
文件大小： 7.02 GB
收录时间： 2022-1-2 01:52
已经下载： 3次
资源热度： 204
最近下载： 2024-5-26 23:48

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[DesireCourse.Net] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence.torrent

1. Welcome/1. Introduction.mp439.16MB

1. Welcome/2. Outline.mp473.71MB

1. Welcome/3. Where to get the code.mp429.5MB

10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp486.54MB

10. GANs (Generative Adversarial Networks)/2. GAN Code.mp478.19MB

11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp437.77MB

11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp437.58MB

11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp461.29MB

11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp455.71MB

11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp449.21MB

11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp437.51MB

11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp497.79MB

11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp442.99MB

11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp448.96MB

11. Deep Reinforcement Learning (Theory)/5. The Return.mp420.95MB

11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp443.29MB

11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp430.36MB

11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp439.03MB

11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp452.55MB

12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp429.65MB

12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp456.03MB

12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.07MB

12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp429.78MB

12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp446.84MB

12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp483.4MB

12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp462.34MB

12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp459.16MB

12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp418.18MB

13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp431.57MB

13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4124.47MB

13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp442.4MB

13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp450.82MB

13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp450.06MB

14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp442.5MB

14. Low-Level Tensorflow/2. Constants and Basic Computation.mp450.23MB

14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp470.62MB

14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp470.18MB

15. In-Depth Loss Functions/1. Mean Squared Error.mp437.34MB

15. In-Depth Loss Functions/2. Binary Cross Entropy.mp423.62MB

15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp435.44MB

16. In-Depth Gradient Descent/1. Gradient Descent.mp434.89MB

16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp425.05MB

16. In-Depth Gradient Descent/3. Momentum.mp439.36MB

16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp438.53MB

16. In-Depth Gradient Descent/5. Adam.mp442.58MB

18. Setting up your Environment/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4166.73MB

18. Setting up your Environment/2. Windows-Focused Environment Setup 2018.mp4193.99MB

18. Setting up your Environment/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4167.3MB

19. Extra Help With Python Coding for Beginners/1. How to Code Yourself (part 1).mp482.13MB

19. Extra Help With Python Coding for Beginners/2. How to Code Yourself (part 2).mp456.42MB

19. Extra Help With Python Coding for Beginners/3. Proof that using Jupyter Notebook is the same as not using it.mp477.94MB

19. Extra Help With Python Coding for Beginners/4. Is Theano Dead.mp444.39MB

2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp465.17MB

2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp451.15MB

2. Google Colab/3. Uploading your own data to Google Colab.mp489.09MB

2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp443.85MB

20. Effective Learning Strategies for Machine Learning/1. How to Succeed in this Course (Long Version).mp438.93MB

20. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4117.08MB

20. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).mp488.15MB

20. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).mp4122.65MB

21. Appendix FAQ/1. What is the Appendix.mp418.05MB

21. Appendix FAQ/2. BONUS Where to get discount coupons and FREE deep learning material.mp437.86MB

3. Machine Learning and Neurons/1. What is Machine Learning.mp473.16MB

3. Machine Learning and Neurons/10. Saving and Loading a Model.mp435.29MB

3. Machine Learning and Neurons/11. Suggestion Box.mp416.13MB

3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp468.51MB

3. Machine Learning and Neurons/3. Beginner's Code Preamble.mp425.12MB

3. Machine Learning and Neurons/4. Classification Notebook.mp466.31MB

3. Machine Learning and Neurons/5. Code Preparation (Regression Theory).mp431.34MB

3. Machine Learning and Neurons/6. Regression Notebook.mp471.76MB

3. Machine Learning and Neurons/7. The Neuron.mp449.44MB

3. Machine Learning and Neurons/8. How does a model learn.mp455.03MB

3. Machine Learning and Neurons/9. Making Predictions.mp441.96MB

4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp432.54MB

4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp446.7MB

4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp456.45MB

4. Feedforward Artificial Neural Networks/4. Activation Functions.mp492.18MB

4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp446.87MB

4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp480.86MB

4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp456.17MB

4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp458.36MB

4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp483.95MB

5. Convolutional Neural Networks/1. What is Convolution (part 1).mp479.87MB

5. Convolutional Neural Networks/10. Batch Normalization.mp423.46MB

5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp486.36MB

5. Convolutional Neural Networks/2. What is Convolution (part 2).mp425.16MB

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

5. Convolutional Neural Networks/4. Convolution on Color Images.mp477.03MB

5. Convolutional Neural Networks/5. CNN Architecture.mp490.94MB

5. Convolutional Neural Networks/6. CNN Code Preparation.mp486.3MB

5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp451.66MB

5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp434.81MB

5. Convolutional Neural Networks/9. Data Augmentation.mp439.17MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4103.2MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp450.31MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp477.66MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4143.13MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp431.51MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp427.45MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp480.05MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp438.2MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp476.75MB

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.mp487.69MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp418.27MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp492.05MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp420.42MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp487.23MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp464.35MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp476.1MB

7. Natural Language Processing (NLP)/1. Embeddings.mp457.97MB

7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp462.94MB

7. Natural Language Processing (NLP)/3. Text Preprocessing.mp436.15MB

7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp460.56MB

7. Natural Language Processing (NLP)/5. CNNs for Text.mp440.44MB

7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp446.41MB

8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp468.74MB

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

9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp455.16MB

9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp431.56MB

9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp436.57MB

9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp420.63MB

9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp466.58MB

9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp446.07MB