Python Deep Learning Solutions Video

1. 1.Deep Learning Frameworks/02.Understanding TensorFlow, Keras and PyTorch Framework.mp423.05MB
2. 1.Deep Learning Frameworks/03.Deep Learning Using CNTK and Gluon Framework.mp49.4MB
3. 2. Feed-Forward Neural Networks/04.Implementing Single and Multi-Layer Neural Network.mp426.97MB
4. 2. Feed-Forward Neural Networks/05.Experiment with Activation Functions, Hidden Layers, and Hidden Units.mp425.07MB
5. 2. Feed-Forward Neural Networks/06.Autoencoder, Loss Function, and Optimizers.mp426.24MB
6. 2. Feed-Forward Neural Networks/07.Overfitting Prevention Methods.mp415.7MB
7. 3.Convolutional and Recurrent Neural Networks/08.Optimization Techniques for CNNs.mp419.19MB
8. 3.Convolutional and Recurrent Neural Networks/09.Experimenting with Different Types of Initialization.mp428.41MB
9. 3.Convolutional and Recurrent Neural Networks/10.Implementing Simple RNN and LSTM.mp416.98MB
10. 3.Convolutional and Recurrent Neural Networks/11.Implementing GRUs and Bidirectional RNNs.mp416.21MB
11. 4.GANs and Computer Vision/12.Implementing Generative Adversarial Networks.mp414.77MB
12. 4.GANs and Computer Vision/13.Computer Vision Techniques.mp421.52MB
13. 4.GANs and Computer Vision/14.Detecting Facial Key Points and Transferring Styles.mp411.94MB
14. 5.Neural Network Learning and Data Processing/15.Hyper Parameter Selection and Tuning.mp418.4MB
15. 5.Neural Network Learning and Data Processing/16.Speech Recognition.mp423.01MB
16. 5.Neural Network Learning and Data Processing/17.Time Series and Structured Data.mp428.85MB
17. 6.Network Internals and Pretrained Models/18.Visualizing and Analysing Network.mp415.6MB
18. 6.Network Internals and Pretrained Models/19.Freezing and Storing the Network.mp410.85MB
19. 6.Network Internals and Pretrained Models/20.Using InceptionV3 and ResNet50 Model.mp411.17MB
20. 6.Network Internals and Pretrained Models/21.Leveraging VGG Model and Fine Tuning.mp419.27MB
21. 1.Deep Learning Frameworks/01.The course overview.mp49.67MB