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Tensorflow 2 0 Deep Learning and Artificial Intelligence
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[TutsNode.com] - Tensorflow 2.0 Deep Learning and Artificial Intelligence (Size: 6.89 GB) (Files: 403)
[TutsNode.com] - Tensorflow 2.0 Deep Learning and Artificial Intelligence
18. Setting up your Environment (FAQ by Student Request)
2. Anaconda Environment Setup.mp4
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4
2. Anaconda Environment Setup.srt
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
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.srt
4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt
3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt
1. How to Succeed in this Course (Long Version).srt
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4
1. How to Succeed in this Course (Long Version).mp4
5. Convolutional Neural Networks
5. CNN Architecture.srt
4. Convolution on Color Images.srt
1. What is Convolution (part 1).srt
6. CNN Code Preparation.srt
11. Improving CIFAR-10 Results.srt
9. Data Augmentation.srt
3. What is Convolution (part 3).srt
7. CNN for Fashion MNIST.srt
2. What is Convolution (part 2).srt
10. Batch Normalization.srt
8. CNN for CIFAR-10.srt
5. CNN Architecture.mp4
1. What is Convolution (part 1).mp4
6. CNN Code Preparation.mp4
11. Improving CIFAR-10 Results.mp4
4. Convolution on Color Images.mp4
7. CNN for Fashion MNIST.mp4
9. Data Augmentation.mp4
8. CNN for CIFAR-10.mp4
3. What is Convolution (part 3).mp4
2. What is Convolution (part 2).mp4
10. Batch Normalization.mp4
3. Machine Learning and Neurons
10. Why Keras.srt
2. Code Preparation (Classification Theory).srt
1. What is Machine Learning.srt
7. How does a model learn.srt
6. The Neuron.srt
5. Regression Notebook.srt
9. Saving and Loading a Model.srt
3. Classification Notebook.srt
4. Code Preparation (Regression Theory).srt
8. Making Predictions.srt
11. Suggestion Box.srt
1. What is Machine Learning.mp4
2. Code Preparation (Classification Theory).mp4
5. Regression Notebook.mp4
3. Classification Notebook.mp4
7. How does a model learn.mp4
6. The Neuron.mp4
8. Making Predictions.mp4
9. Saving and Loading a Model.mp4
4. Code Preparation (Regression Theory).mp4
11. Suggestion Box.mp4
10. Why Keras.mp4
1. Welcome
3.1 Colab Notebooks.html
3.2 Github Link.html
2. Outline.srt
3. Where to get the code.srt
1. Introduction.srt
2. Outline.mp4
3. Where to get the code.mp4
1. Introduction.mp4
11. Deep Reinforcement Learning (Theory)
2. Elements of a Reinforcement Learning Problem.srt
11. Q-Learning.srt
12. Deep Q-Learning DQN (pt 1).srt
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt
13. Deep Q-Learning DQN (pt 2).srt
4. Markov Decision Processes (MDPs).srt
6. Value Functions and the Bellman Equation.srt
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt
3. States, Actions, Rewards, Policies.srt
7. What does it mean to “learnâ€.srt
1. Deep Reinforcement Learning Section Introduction.srt
14. How to Learn Reinforcement Learning.srt
10. Epsilon-Greedy.srt
5. The Return.srt
2. Elements of a Reinforcement Learning Problem.mp4
11. Q-Learning.mp4
12. Deep Q-Learning DQN (pt 1).mp4
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4
13. Deep Q-Learning DQN (pt 2).mp4
4. Markov Decision Processes (MDPs).mp4
6. Value Functions and the Bellman Equation.mp4
3. States, Actions, Rewards, Policies.mp4
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4
10. Epsilon-Greedy.mp4
1. Deep Reinforcement Learning Section Introduction.mp4
14. How to Learn Reinforcement Learning.mp4
7. What does it mean to “learnâ€.mp4
5. The Return.mp4
6. Recurrent Neural Networks, Time Series, and Sequence Data
5. Recurrent Neural Networks.srt
1. Sequence Data.srt
12. Demo of the Long Distance Problem.srt
9. GRU and LSTM (pt 1).srt
15. Stock Return Predictions using LSTMs (pt 1).srt
17. Stock Return Predictions using LSTMs (pt 3).srt
10. GRU and LSTM (pt 2).srt
3. Autoregressive Linear Model for Time Series Prediction.srt
2. Forecasting.srt
7. RNN for Time Series Prediction.srt
12. Demo of the Long Distance Problem.mp4
8. Paying Attention to Shapes.srt
11. A More Challenging Sequence.srt
18. Other Ways to Forecast.srt
6. RNN Code Preparation.srt
16. Stock Return Predictions using LSTMs (pt 2).srt
13. RNN for Image Classification (Theory).srt
4. Proof that the Linear Model Works.srt
14. RNN for Image Classification (Code).srt
1. Sequence Data.mp4
5. Recurrent Neural Networks.mp4
9. GRU and LSTM (pt 1).mp4
7. RNN for Time Series Prediction.mp4
3. Autoregressive Linear Model for Time Series Prediction.mp4
17. Stock Return Predictions using LSTMs (pt 3).mp4
15. Stock Return Predictions using LSTMs (pt 1).mp4
11. A More Challenging Sequence.mp4
8. Paying Attention to Shapes.mp4
10. GRU and LSTM (pt 2).mp4
2. Forecasting.mp4
16. Stock Return Predictions using LSTMs (pt 2).mp4
13. RNN for Image Classification (Theory).mp4
18. Other Ways to Forecast.mp4
14. RNN for Image Classification (Code).mp4
6. RNN Code Preparation.mp4
4. Proof that the Linear Model Works.mp4
4. Feedforward Artificial Neural Networks
5. Activation Functions.srt
2. Beginners Rejoice The Math in This Course is Optional.srt
8. Code Preparation (ANN).srt
7. How to Represent Images.srt
10. ANN for Regression.srt
3. Forward Propagation.srt
4. The Geometrical Picture.srt
6. Multiclass Classification.srt
9. ANN for Image Classification.srt
1. Artificial Neural Networks Section Introduction.srt
5. Activation Functions.mp4
7. How to Represent Images.mp4
10. ANN for Regression.mp4
2. Beginners Rejoice The Math in This Course is Optional.mp4
4. The Geometrical Picture.mp4
8. Code Preparation (ANN).mp4
9. ANN for Image Classification.mp4
3. Forward Propagation.mp4
6. Multiclass Classification.mp4
1. Artificial Neural Networks Section Introduction.mp4
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)
2. How to Code Yourself (part 1).srt
1. Beginner's Coding Tips.srt
4. Proof that using Jupyter Notebook is the same as not using it.srt
3. How to Code Yourself (part 2).srt
5. Is Theano Dead.srt
1. Beginner's Coding Tips.mp4
2. How to Code Yourself (part 1).mp4
4. Proof that using Jupyter Notebook is the same as not using it.mp4
3. How to Code Yourself (part 2).mp4
5. Is Theano Dead.mp4
10. GANs (Generative Adversarial Networks)
1. GAN Theory.srt
2. GAN Code.srt
1. GAN Theory.mp4
2. GAN Code.mp4
13. Advanced Tensorflow Usage
2. Tensorflow Serving pt 2.srt
4. Why is Google the King of Distributed Computing.srt
3. Tensorflow Lite (TFLite).srt
5. Training with Distributed Strategies.srt
1. What is a Web Service (Tensorflow Serving pt 1).srt
6. Using the TPU.srt
2. Tensorflow Serving pt 2.mp4
6. Using the TPU.mp4
4. Why is Google the King of Distributed Computing.mp4
5. Training with Distributed Strategies.mp4
3. Tensorflow Lite (TFLite).mp4
1. What is a Web Service (Tensorflow Serving pt 1).mp4
8. Recommender Systems
1. Recommender Systems with Deep Learning Theory.srt
2. Recommender Systems with Deep Learning Code.srt
1. Recommender Systems with Deep Learning Theory.mp4
2. Recommender Systems with Deep Learning Code.mp4
7. Natural Language Processing (NLP)
2. Code Preparation (NLP).srt
1. Embeddings.srt
5. CNNs for Text.srt
4. Text Classification with LSTMs.srt
6. Text Classification with CNNs.srt
3. Text Preprocessing.srt
2. Code Preparation (NLP).mp4
1. Embeddings.mp4
4. Text Classification with LSTMs.mp4
5. CNNs for Text.mp4
6. Text Classification with CNNs.mp4
3. Text Preprocessing.mp4
16. In-Depth Gradient Descent
5. Adam (pt 1).srt
4. Variable and Adaptive Learning Rates.srt
6. Adam (pt 2).srt
1. Gradient Descent.srt
3. Momentum.srt
2. Stochastic Gradient Descent.srt
5. Adam (pt 1).mp4
6. Adam (pt 2).mp4
1. Gradient Descent.mp4
4. Variable and Adaptive Learning Rates.mp4
3. Momentum.mp4
2. Stochastic Gradient Descent.mp4
12. Stock Trading Project with Deep Reinforcement Learning
2. Data and Environment.srt
6. Code pt 2.srt
10. Help! Why is the code slower on my machine.srt
4. Program Design and Layout.srt
8. Code pt 4.srt
7. Code pt 3.srt
5. Code pt 1.srt
3. Replay Buffer.srt
1. Reinforcement Learning Stock Trader Introduction.srt
9. Reinforcement Learning Stock Trader Discussion.srt
6. Code pt 2.mp4
8. Code pt 4.mp4
7. Code pt 3.mp4
2. Data and Environment.mp4
10. Help! Why is the code slower on my machine.mp4
5. Code pt 1.mp4
1. Reinforcement Learning Stock Trader Introduction.mp4
4. Program Design and Layout.mp4
3. Replay Buffer.mp4
9. Reinforcement Learning Stock Trader Discussion.mp4
21. Appendix FAQ Finale
1. What is the Appendix.srt
2. BONUS Lecture.srt
2. BONUS Lecture.mp4
1. What is the Appendix.mp4
2. Google Colab
1. Intro to Google Colab, how to use a GPU or TPU for free.srt
3. Uploading your own data to Google Colab.srt
4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt
2. Tensorflow 2.0 in Google Colab.srt
5. How to Succeed in this Course.srt
3. Uploading your own data to Google Colab.mp4
1. Intro to Google Colab, how to use a GPU or TPU for free.mp4
5. How to Succeed in this Course.mp4
2. Tensorflow 2.0 in Google Colab.mp4
4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4
9. Transfer Learning for Computer Vision
5. Transfer Learning Code (pt 1).srt
1. Transfer Learning Theory.srt
6. Transfer Learning Code (pt 2).srt
3. Large Datasets and Data Generators.srt
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt
4. 2 Approaches to Transfer Learning.srt
5. Transfer Learning Code (pt 1).mp4
1. Transfer Learning Theory.mp4
6. Transfer Learning Code (pt 2).mp4
3. Large Datasets and Data Generators.mp4
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4
4. 2 Approaches to Transfer Learning.mp4
14. Low-Level Tensorflow
3. Variables and Gradient Tape.srt
4. Build Your Own Custom Model.srt
1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt
2. Constants and Basic Computation.srt
4. Build Your Own Custom Model.mp4
3. Variables and Gradient Tape.mp4
2. Constants and Basic Computation.mp4
1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4
15. In-Depth Loss Functions
1. Mean Squared Error.srt
3. Categorical Cross Entropy.srt
2. Binary Cross Entropy.srt
1. Mean Squared Error.mp4
3. Categorical Cross Entropy.mp4
2. Binary Cross Entropy.mp4
17. Extras
1. How to Choose Hyperparameters.srt
3. Links to TF2.0 Notebooks.html
2. Where Are The Exercises.srt
1. How to Choose Hyperparameters.mp4
2. Where Are The Exercises.mp4
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