[Tutorialsplanet.NET] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence

磁力链接/BT种子名称

[Tutorialsplanet.NET] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence

磁力链接/BT种子简介

种子哈希：dbb92c8e7b97c36fa60e25a923e6f31cd340c6cd
文件大小： 6.96G
已经下载：425次
下载速度：极快
收录时间：2021-03-12
最近下载：2024-11-13

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文件列表

  • 18. Setting up your Environment/2. Windows-Focused Environment Setup 2018.mp4 203.4 MB
  • 18. Setting up your Environment/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 175.4 MB
  • 18. Setting up your Environment/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 174.8 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 150.1 MB
  • 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 130.5 MB
  • 19. Appendix FAQ/9. What order should I take your courses in (part 2).mp4 128.6 MB
  • 19. Appendix FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 122.8 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 108.2 MB
  • 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 102.5 MB
  • 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 96.6 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 96.5 MB
  • 5. Convolutional Neural Networks/5. CNN Architecture.mp4 95.4 MB
  • 2. Google Colab/3. Uploading your own data to Google Colab.mp4 93.4 MB
  • 19. Appendix FAQ/8. What order should I take your courses in (part 1).mp4 92.4 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 91.9 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 91.5 MB
  • 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 90.7 MB
  • 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 90.5 MB
  • 5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 90.5 MB
  • 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 88.0 MB
  • 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 87.6 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 87.4 MB
  • 19. Appendix FAQ/3. How to Code Yourself (part 1).mp4 86.1 MB
  • 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 84.8 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 83.9 MB
  • 10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 82.0 MB
  • 19. Appendix FAQ/5. Proof that using Jupyter Notebook is the same as not using it.mp4 81.7 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 81.4 MB
  • 5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 80.8 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 80.5 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 79.8 MB
  • 1. Welcome/2. Outline.mp4 77.3 MB
  • 3. Machine Learning and Neurons/1. What is Machine Learning.mp4 76.7 MB
  • 3. Machine Learning and Neurons/5. Regression Notebook.mp4 75.2 MB
  • 14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 74.0 MB
  • 14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 73.6 MB
  • 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 72.1 MB
  • 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 71.8 MB
  • 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 69.8 MB
  • 3. Machine Learning and Neurons/3. Classification Notebook.mp4 69.5 MB
  • 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 68.3 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 67.5 MB
  • 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 66.0 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 65.4 MB
  • 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 64.3 MB
  • 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 63.5 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 62.0 MB
  • 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 61.6 MB
  • 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 61.2 MB
  • 7. Natural Language Processing (NLP)/1. Embeddings.mp4 60.8 MB
  • 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 59.2 MB
  • 19. Appendix FAQ/4. How to Code Yourself (part 2).mp4 59.1 MB
  • 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 58.9 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 58.7 MB
  • 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 58.4 MB
  • 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 57.8 MB
  • 3. Machine Learning and Neurons/7. How does a model learn.mp4 57.7 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 56.2 MB
  • 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 55.1 MB
  • 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 54.2 MB
  • 2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 53.6 MB
  • 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 53.3 MB
  • 14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 52.7 MB
  • 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 52.5 MB
  • 3. Machine Learning and Neurons/6. The Neuron.mp4 51.8 MB
  • 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 51.7 MB
  • 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 51.6 MB
  • 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 51.3 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 49.5 MB
  • 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 49.2 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 49.1 MB
  • 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 48.7 MB
  • 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 48.3 MB
  • 19. Appendix FAQ/7. Is Theano Dead.mp4 46.5 MB
  • 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 46.0 MB
  • 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 45.4 MB
  • 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 45.1 MB
  • 16. In-Depth Gradient Descent/5. Adam.mp4 44.6 MB
  • 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 44.6 MB
  • 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 44.4 MB
  • 3. Machine Learning and Neurons/8. Making Predictions.mp4 44.0 MB
  • 7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 42.8 MB
  • 16. In-Depth Gradient Descent/3. Momentum.mp4 41.3 MB
  • 5. Convolutional Neural Networks/9. Data Augmentation.mp4 41.1 MB
  • 1. Welcome/1. Introduction.mp4 41.1 MB
  • 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 40.9 MB
  • 19. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp4 40.8 MB
  • 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 40.4 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 40.0 MB
  • 19. Appendix FAQ/10. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
  • 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 39.6 MB
  • 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 39.4 MB
  • 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 39.3 MB
  • 15. In-Depth Loss Functions/1. Mean Squared Error.mp4 39.2 MB
  • 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 38.3 MB
  • 7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 37.9 MB
  • 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 37.2 MB
  • 3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 37.0 MB
  • 16. In-Depth Gradient Descent/1. Gradient Descent.mp4 36.6 MB
  • 5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 36.5 MB
  • 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 34.1 MB
  • 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 33.1 MB
  • 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 33.1 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 33.0 MB
  • 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 32.8 MB
  • 1. Welcome/3. Where to get the code.mp4 32.0 MB
  • 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 31.8 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 31.2 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 31.1 MB
  • 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 29.0 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 28.8 MB
  • 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 26.4 MB
  • 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 26.3 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 25.2 MB
  • 5. Convolutional Neural Networks/10. Batch Normalization.mp4 24.6 MB
  • 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 22.5 MB
  • 11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 22.0 MB
  • 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 21.6 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 21.4 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 19.2 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 19.1 MB
  • 19. Appendix FAQ/1. What is the Appendix.mp4 18.9 MB
  • 18. Setting up your Environment/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32.8 kB
  • 19. Appendix FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.4 kB
  • 5. Convolutional Neural Networks/5. CNN Architecture.srt 28.6 kB
  • 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26.8 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 26.2 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 24.6 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.srt 23.6 kB
  • 19. Appendix FAQ/9. What order should I take your courses in (part 2).srt 23.6 kB
  • 4. Feedforward Artificial Neural Networks/4. Activation Functions.srt 23.2 kB
  • 19. Appendix FAQ/3. How to Code Yourself (part 1).srt 22.7 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 21.6 kB
  • 10. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 21.2 kB
  • 5. Convolutional Neural Networks/4. Convolution on Color Images.srt 21.0 kB
  • 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.srt 20.9 kB
  • 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).srt 20.7 kB
  • 5. Convolutional Neural Networks/1. What is Convolution (part 1).srt 20.6 kB
  • 18. Setting up your Environment/2. Windows-Focused Environment Setup 2018.srt 20.4 kB
  • 5. Convolutional Neural Networks/6. CNN Code Preparation.srt 20.1 kB
  • 3. Machine Learning and Neurons/1. What is Machine Learning.srt 18.9 kB
  • 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 18.3 kB
  • 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 17.8 kB
  • 1. Welcome/2. Outline.srt 17.5 kB
  • 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).srt 17.2 kB
  • 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16.8 kB
  • 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).srt 16.7 kB
  • 7. Natural Language Processing (NLP)/1. Embeddings.srt 16.6 kB
  • 19. Appendix FAQ/8. What order should I take your courses in (part 1).srt 16.5 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).srt 16.1 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 16.1 kB
  • 4. Feedforward Artificial Neural Networks/6. How to Represent Images.srt 16.0 kB
  • 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15.5 kB
  • 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15.2 kB
  • 10. GANs (Generative Adversarial Networks)/2. GAN Code.srt 15.2 kB
  • 18. Setting up your Environment/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.0 kB
  • 19. Appendix FAQ/6. How to Succeed in this Course (Long Version).srt 15.0 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).srt 14.8 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 14.7 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 14.6 kB
  • 19. Appendix FAQ/5. Proof that using Jupyter Notebook is the same as not using it.srt 14.6 kB
  • 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14.5 kB
  • 3. Machine Learning and Neurons/7. How does a model learn.srt 14.3 kB
  • 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 14.1 kB
  • 14. Low-Level Tensorflow/3. Variables and Gradient Tape.srt 13.9 kB
  • 16. In-Depth Gradient Descent/5. Adam.srt 13.8 kB
  • 14. Low-Level Tensorflow/4. Build Your Own Custom Model.srt 13.6 kB
  • 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13.5 kB
  • 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.srt 13.5 kB
  • 19. Appendix FAQ/4. How to Code Yourself (part 2).srt 13.3 kB
  • 4. Feedforward Artificial Neural Networks/9. ANN for Regression.srt 13.1 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 13.0 kB
  • 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 13.0 kB
  • 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 13.0 kB
  • 19. Appendix FAQ/7. Is Theano Dead.srt 12.9 kB
  • 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 12.8 kB
  • 3. Machine Learning and Neurons/6. The Neuron.srt 12.8 kB
  • 4. Feedforward Artificial Neural Networks/2. Forward Propagation.srt 12.5 kB
  • 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt 12.5 kB
  • 3. Machine Learning and Neurons/5. Regression Notebook.srt 12.4 kB
  • 2. Google Colab/3. Uploading your own data to Google Colab.srt 12.3 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 12.0 kB
  • 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.srt 12.0 kB
  • 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 11.8 kB
  • 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.srt 11.8 kB
  • 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11.6 kB
  • 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.srt 11.5 kB
  • 5. Convolutional Neural Networks/9. Data Augmentation.srt 11.5 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 11.5 kB
  • 15. In-Depth Loss Functions/1. Mean Squared Error.srt 11.5 kB
  • 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).srt 11.3 kB
  • 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.srt 11.2 kB
  • 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 10.9 kB
  • 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).srt 10.7 kB
  • 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.srt 10.2 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 10.1 kB
  • 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.srt 10.0 kB
  • 16. In-Depth Gradient Descent/1. Gradient Descent.srt 10.0 kB
  • 14. Low-Level Tensorflow/2. Constants and Basic Computation.srt 9.9 kB
  • 15. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 9.9 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 9.8 kB
  • 7. Natural Language Processing (NLP)/5. CNNs for Text.srt 9.8 kB
  • 2. Google Colab/2. Tensorflow 2.0 in Google Colab.srt 9.7 kB
  • 3. Machine Learning and Neurons/3. Classification Notebook.srt 9.6 kB
  • 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).srt 9.3 kB
  • 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.srt 9.1 kB
  • 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.srt 9.0 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.srt 8.8 kB
  • 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.srt 8.8 kB
  • 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.srt 8.7 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.srt 8.4 kB
  • 5. Convolutional Neural Networks/3. What is Convolution (part 3).srt 8.2 kB
  • 3. Machine Learning and Neurons/8. Making Predictions.srt 8.2 kB
  • 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.srt 8.2 kB
  • 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.srt 8.1 kB
  • 19. Appendix FAQ/10. BONUS Where to get discount coupons and FREE deep learning material.srt 8.1 kB
  • 16. In-Depth Gradient Descent/3. Momentum.srt 8.0 kB
  • 17. Extras/1. Links to TF2.0 Notebooks.html 8.0 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.srt 7.9 kB
  • 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).srt 7.9 kB
  • 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.srt 7.8 kB
  • 1. Welcome/3. Where to get the code.srt 7.8 kB
  • 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.srt 7.6 kB
  • 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 7.5 kB
  • 15. In-Depth Loss Functions/2. Binary Cross Entropy.srt 7.4 kB
  • 5. Convolutional Neural Networks/2. What is Convolution (part 2).srt 7.4 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 7.4 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 7.3 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.srt 7.1 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.srt 7.0 kB
  • 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.srt 6.8 kB
  • 5. Convolutional Neural Networks/10. Batch Normalization.srt 6.7 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).srt 6.7 kB
  • 11. Deep Reinforcement Learning (Theory)/5. The Return.srt 6.4 kB
  • 7. Natural Language Processing (NLP)/3. Text Preprocessing.srt 6.3 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).srt 6.1 kB
  • 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.srt 6.1 kB
  • 1. Welcome/1. Introduction.srt 5.8 kB
  • 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.srt 5.5 kB
  • 5. Convolutional Neural Networks/8. CNN for CIFAR-10.srt 5.5 kB
  • 3. Machine Learning and Neurons/9. Saving and Loading a Model.srt 5.0 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.srt 4.7 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.srt 4.5 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).srt 4.3 kB
  • 19. Appendix FAQ/1. What is the Appendix.srt 3.8 kB
  • 13. Advanced Tensorflow Usage/6. Using the TPU.html 1.8 kB
  • [Tutorialsplanet.NET].url 128 Bytes

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