GetFreeCourses.Co-Udemy-Tensorflow 2.0 Deep Learning and Artificial Intelligence

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Last Access Time	Oct. 15, 2025, 9:42 a.m.
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18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4 180.9 MB
18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 167.3 MB
18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 150.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 124.0 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108.2 MB
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.mp4 105.6 MB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 105.0 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 98.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 90.1 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 87.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 83.0 MB
5. Convolutional Neural Networks/5. CNN Architecture.mp4 80.6 MB
4. Feedforward Artificial Neural Networks/5. Activation Functions.mp4 80.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 79.9 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 79.8 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 79.7 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 78.3 MB
5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 76.9 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 75.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 74.1 MB
1. Welcome/2. Outline.mp4 73.7 MB
2. Google Colab/3. Uploading your own data to Google Colab.mp4 73.6 MB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 72.9 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp4 71.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 71.7 MB
4. Feedforward Artificial Neural Networks/7. How to Represent Images.mp4 70.5 MB
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.mp4 69.4 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 69.4 MB
4. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 69.3 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 68.7 MB
4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.mp4 68.5 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 68.0 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 67.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 67.1 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 66.5 MB
3. Machine Learning and Neurons/1. What is Machine Learning.mp4 65.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 64.6 MB
1. Welcome/3. Where to get the code.mp4 62.9 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 61.8 MB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 59.8 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 58.8 MB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 58.5 MB
3. Machine Learning and Neurons/5. Regression Notebook.mp4 57.5 MB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 57.0 MB
4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.mp4 56.4 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 56.3 MB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 56.0 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 55.1 MB
16. In-Depth Gradient Descent/5. Adam (pt 1).mp4 55.1 MB
3. Machine Learning and Neurons/3. Classification Notebook.mp4 54.5 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 53.8 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 52.9 MB
16. In-Depth Gradient Descent/6. Adam (pt 2).mp4 52.8 MB
7. Natural Language Processing (NLP)/1. Embeddings.mp4 52.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 52.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 52.5 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 52.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 51.0 MB
4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 50.9 MB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 50.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 50.4 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 49.6 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 49.3 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp4 49.1 MB
3. Machine Learning and Neurons/7. How does a model learn.mp4 48.0 MB
4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 47.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 46.8 MB
4. Feedforward Artificial Neural Networks/3. Forward Propagation.mp4 46.7 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 46.1 MB
13. Advanced Tensorflow Usage/6. Using the TPU.mp4 45.2 MB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 44.9 MB
2. Google Colab/5. How to Succeed in this Course.mp4 43.8 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 43.6 MB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 43.5 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 43.3 MB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 42.8 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 42.7 MB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 42.6 MB
3. Machine Learning and Neurons/6. The Neuron.mp4 42.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.mp4 42.5 MB
4. Feedforward Artificial Neural Networks/6. Multiclass Classification.mp4 41.4 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp4 40.8 MB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 40.7 MB
7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 40.4 MB
14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 40.3 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 40.1 MB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 39.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 39.5 MB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 38.9 MB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 38.7 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 38.1 MB
17. Extras/1. How to Choose Hyperparameters.mp4 37.9 MB
21. Appendix FAQ Finale/2. BONUS Lecture.mp4 37.8 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 37.7 MB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 36.6 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 35.2 MB
5. Convolutional Neural Networks/9. Data Augmentation.mp4 35.0 MB
16. In-Depth Gradient Descent/1. Gradient Descent.mp4 34.9 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 34.9 MB
1. Welcome/1. Introduction.mp4 34.8 MB
16. In-Depth Gradient Descent/3. Momentum.mp4 34.3 MB
3. Machine Learning and Neurons/8. Making Predictions.mp4 33.9 MB
15. In-Depth Loss Functions/1. Mean Squared Error.mp4 33.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 33.0 MB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 31.7 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 31.7 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 31.6 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 29.8 MB
3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 29.7 MB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 29.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 29.1 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 28.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp4 28.3 MB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 27.8 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 27.6 MB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 27.3 MB
3. Machine Learning and Neurons/11. Suggestion Box.mp4 27.1 MB
3. Machine Learning and Neurons/10. Why Keras.mp4 26.5 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 26.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 26.0 MB
17. Extras/2. Where Are The Exercises.mp4 26.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 24.0 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 23.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 23.3 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 23.0 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 22.3 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 21.1 MB
5. Convolutional Neural Networks/10. Batch Normalization.mp4 21.1 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 20.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 18.4 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 16.6 MB
21. Appendix FAQ Finale/1. What is the Appendix.mp4 16.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 16.2 MB
18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32.0 KB
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 31.6 KB
5. Convolutional Neural Networks/5. CNN Architecture.srt 27.9 KB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26.2 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 25.6 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 24.0 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.srt 23.1 KB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.0 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 22.8 KB
4. Feedforward Artificial Neural Networks/5. Activation Functions.srt 22.6 KB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).srt 22.1 KB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 20.7 KB
5. Convolutional Neural Networks/4. Convolution on Color Images.srt 20.6 KB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.srt 20.4 KB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).srt 20.3 KB
5. Convolutional Neural Networks/1. What is Convolution (part 1).srt 20.2 KB
18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.srt 20.0 KB
5. Convolutional Neural Networks/6. CNN Code Preparation.srt 19.6 KB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.srt 19.0 KB
3. Machine Learning and Neurons/1. What is Machine Learning.srt 18.4 KB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 17.9 KB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 17.4 KB
1. Welcome/2. Outline.srt 17.1 KB
4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.srt 17.0 KB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).srt 16.8 KB
16. In-Depth Gradient Descent/5. Adam (pt 1).srt 16.7 KB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16.4 KB
4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).srt 16.3 KB
7. Natural Language Processing (NLP)/1. Embeddings.srt 16.2 KB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.1 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).srt 15.7 KB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 15.7 KB
4. Feedforward Artificial Neural Networks/7. How to Represent Images.srt 15.6 KB
1. Welcome/3. Where to get the code.srt 15.4 KB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15.2 KB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 14.9 KB
10. GANs (Generative Adversarial Networks)/2. GAN Code.srt 14.9 KB
18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.7 KB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 14.6 KB
16. In-Depth Gradient Descent/6. Adam (pt 2).srt 14.5 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).srt 14.4 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 14.3 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 14.2 KB
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.srt 14.2 KB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14.1 KB
3. Machine Learning and Neurons/7. How does a model learn.srt 14.0 KB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 13.8 KB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.srt 13.6 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 13.3 KB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.srt 13.3 KB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13.2 KB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.srt 13.2 KB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).srt 13.0 KB
4. Feedforward Artificial Neural Networks/10. ANN for Regression.srt 12.8 KB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 12.7 KB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.srt 12.6 KB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 12.5 KB
3. Machine Learning and Neurons/6. The Neuron.srt 12.5 KB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12.4 KB
4. Feedforward Artificial Neural Networks/3. Forward Propagation.srt 12.2 KB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt 12.2 KB
3. Machine Learning and Neurons/5. Regression Notebook.srt 12.1 KB
2. Google Colab/3. Uploading your own data to Google Colab.srt 12.0 KB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 11.8 KB
12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.srt 11.7 KB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.srt 11.7 KB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 11.5 KB
4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.srt 11.5 KB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11.3 KB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.srt 11.3 KB
5. Convolutional Neural Networks/9. Data Augmentation.srt 11.2 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 11.2 KB
15. In-Depth Loss Functions/1. Mean Squared Error.srt 11.2 KB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).srt 11.0 KB
4. Feedforward Artificial Neural Networks/6. Multiclass Classification.srt 11.0 KB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 10.7 KB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).srt 10.4 KB
7. Natural Language Processing (NLP)/5. CNNs for Text.srt 10.1 KB
4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.srt 9.9 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 9.9 KB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.srt 9.8 KB
16. In-Depth Gradient Descent/1. Gradient Descent.srt 9.8 KB
14. Low-Level Tensorflow/2. Constants and Basic Computation.srt 9.6 KB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 9.6 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 9.6 KB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.srt 9.5 KB
3. Machine Learning and Neurons/3. Classification Notebook.srt 9.4 KB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).srt 9.1 KB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.srt 8.9 KB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.srt 8.8 KB
17. Extras/1. How to Choose Hyperparameters.srt 8.7 KB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.srt 8.6 KB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.srt 8.6 KB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.srt 8.5 KB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.srt 8.4 KB
2. Google Colab/5. How to Succeed in this Course.srt 8.3 KB
17. Extras/3. Links to TF2.0 Notebooks.html 8.1 KB
5. Convolutional Neural Networks/3. What is Convolution (part 3).srt 8.0 KB
3. Machine Learning and Neurons/8. Making Predictions.srt 8.0 KB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.srt 8.0 KB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.srt 7.9 KB
21. Appendix FAQ Finale/2. BONUS Lecture.srt 7.9 KB
16. In-Depth Gradient Descent/3. Momentum.srt 7.8 KB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.srt 7.8 KB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).srt 7.7 KB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.srt 7.6 KB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.srt 7.5 KB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 7.3 KB
15. In-Depth Loss Functions/2. Binary Cross Entropy.srt 7.3 KB
5. Convolutional Neural Networks/2. What is Convolution (part 2).srt 7.2 KB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 7.2 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.srt 7.2 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 7.1 KB
13. Advanced Tensorflow Usage/6. Using the TPU.srt 7.0 KB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.srt 6.9 KB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.srt 6.8 KB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.srt 6.6 KB
5. Convolutional Neural Networks/10. Batch Normalization.srt 6.5 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).srt 6.5 KB
11. Deep Reinforcement Learning (Theory)/5. The Return.srt 6.3 KB
7. Natural Language Processing (NLP)/3. Text Preprocessing.srt 6.2 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).srt 6.0 KB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.srt 6.0 KB
3. Machine Learning and Neurons/10. Why Keras.srt 5.8 KB
1. Welcome/1. Introduction.srt 5.7 KB
17. Extras/2. Where Are The Exercises.srt 5.4 KB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.srt 5.4 KB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.srt 5.4 KB
3. Machine Learning and Neurons/9. Saving and Loading a Model.srt 4.9 KB
3. Machine Learning and Neurons/11. Suggestion Box.srt 4.7 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.srt 4.6 KB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.srt 4.4 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).srt 4.2 KB
21. Appendix FAQ Finale/1. What is the Appendix.srt 3.7 KB
13. Advanced Tensorflow Usage/How you can help GetFreeCourses.Co.txt 182 bytes
5. Convolutional Neural Networks/How you can help GetFreeCourses.Co.txt 182 bytes
How you can help GetFreeCourses.Co.txt 182 bytes
1. Welcome/3.1 Colab Notebooks.html 157 bytes
1. Welcome/3.2 Github Link.html 120 bytes
13. Advanced Tensorflow Usage/GetFreeCourses.Co.url 116 bytes
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/GetFreeCourses.Co.url 116 bytes
5. Convolutional Neural Networks/GetFreeCourses.Co.url 116 bytes
Download Paid Udemy Courses For Free.url 116 bytes
GetFreeCourses.Co.url 116 bytes

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