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- [ FreeCourseWeb.com ] The Deep Learning with Keras Workshop- An interactive approach to undersanding Deep learning with Keras, 2nd Edition.zip 245.6 MB
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- ~Get Your Files Here !/5 - Building Recurrent Neural Network/12 - Explanation and Problem Description.mp4 17.2 MB
- ~Get Your Files Here !/7 - Conclusion/16 - Conclusion.mp4 6.8 MB
- ~Get Your Files Here !/7 - Conclusion/17 - Bonus Lecture Course discounts newsletter.mp4 814.2 kB
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- [ FreeCourseWeb.com ] Udemy - Artificial Intelligence #5- MLP Networks with Scikit & Keras (Updated).rar 887.6 MB
- 11. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 226.7 MB
- 9. R - Building and training the Model/1. Building, Compiling and Training.mp4 137.1 MB
- 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
- 8. R - Dataset for classification problem/1. Data Normalization and Test-Train Split.mp4 117.2 MB
- 19. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 106.5 MB
- 9. R - Building and training the Model/2. Evaluating and Predicting.mp4 104.0 MB
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- 14. Creating CNN model in R/2. Data Preprocessing.mp4 70.3 MB
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- 5. Important concepts Common Interview questions/1. Some Important Concepts.mp4 65.2 MB
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- 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 63.3 MB
- 2. Setting Up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4 63.0 MB
- 17. Project Data Augmentation for avoiding overfitting/1. Project in R - Data Augmentation.mp4 59.1 MB
- 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4 203.6 MB
- 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4 196.5 MB
- 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4 153.4 MB
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- 16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4 147.0 MB
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- 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4 143.1 MB
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- 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4 123.6 MB
- 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4 120.4 MB
- 7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4 120.3 MB
- 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4 117.6 MB
- 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4 116.4 MB
- 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4 113.1 MB
- 7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp4 104.8 MB
- 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp4 103.5 MB
- 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp4 102.6 MB
- 7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp4 101.8 MB
- 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp4 99.7 MB
- 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp4 98.9 MB
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- Code.zip 97.4 MB
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- 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4 203.6 MB
- 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4 196.5 MB
- 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4 153.4 MB
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- 7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4 143.5 MB
- 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4 143.1 MB
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- 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4 123.6 MB
- 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4 120.4 MB
- 7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4 120.3 MB
- 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4 117.6 MB
- 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4 116.4 MB
- 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4 113.1 MB
- 7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp4 104.8 MB
- 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp4 103.5 MB
- 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp4 102.6 MB
- 7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp4 101.8 MB
- 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp4 99.7 MB
- 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp4 98.9 MB
- 11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4 163.5 MB
- 13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 158.9 MB
- 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
- 15. Add-on 1 Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4 105.3 MB
- 16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 96.6 MB
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- 7. One-Shot and Deep Semi-Supervised Learning/Semi-Supervised Learning Using GANs - Mastering Keras [Video].mp4 20.1 MB
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