CourseClub NET Coursera - Introduction to Deep Learning

1. 001.Specialization Promo/001. Welcome to AML specialization!.mp413.67MB
2. 002.Course intro/002. Course intro.mp422.1MB
3. 003.Linear model as the simplest neural network/003. Linear regression.mp435.73MB
4. 003.Linear model as the simplest neural network/004. Linear classification.mp442.66MB
5. 003.Linear model as the simplest neural network/005. Gradient descent.mp418.96MB
6. 004.Regularization in machine learning/006. Overfitting problem and model validation.mp426.42MB
7. 004.Regularization in machine learning/007. Model regularization.mp419.85MB
8. 005.Stochastic methods for optimization/008. Stochastic gradient descent.mp421.1MB
9. 005.Stochastic methods for optimization/009. Gradient descent extensions.mp436.57MB
10. 006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp444.68MB
11. 006.The simplest neural network MLP/011. Chain rule.mp426.59MB
12. 006.The simplest neural network MLP/012. Backpropagation.mp431.63MB
13. 007.Matrix derivatives/013. Efficient MLP implementation.mp447.09MB
14. 007.Matrix derivatives/014. Other matrix derivatives.mp421.42MB
15. 008.TensorFlow framework/015. What is TensorFlow.mp439.44MB
16. 008.TensorFlow framework/016. Our first model in TensorFlow.mp436.8MB
17. 009.Philosophy of deep learning/017. What Deep Learning is and is not.mp429.46MB
18. 009.Philosophy of deep learning/018. Deep learning as a language.mp424.6MB
19. 010.Introduction to CNN/019. Motivation for convolutional layers.mp441.38MB
20. 010.Introduction to CNN/020. Our first CNN architecture.mp442.57MB
21. 011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp457.9MB
22. 011.Modern CNNs/022. Overview of modern CNN architectures.mp432.24MB
23. 012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp419.28MB
24. 012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp430.74MB
25. 013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp423.78MB
26. 013.Intro to Unsupervised Learning/026. Autoencoders 101.mp422.14MB
27. 014.More Autoencoders/027. Autoencoder applications.mp440.85MB
28. 014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp428.21MB
29. 015.Word Embeddings/029. Natural language processing primer.mp436.68MB
30. 015.Word Embeddings/030. Word embeddings.mp448.35MB
31. 016.Generative Adversarial Networks/031. Generative models 101.mp426.68MB
32. 016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp436.16MB
33. 016.Generative Adversarial Networks/033. Applications of adversarial approach.mp441.89MB
34. 017.Introduction to RNN/034. Motivation for recurrent layers.mp430.15MB
35. 017.Introduction to RNN/035. Simple RNN and Backpropagation.mp435.07MB
36. 018.Modern RNNs/036. The training of RNNs is not that easy.mp426.39MB
37. 018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp434.86MB
38. 018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp447.7MB
39. 019.Applications of RNNs/039. Practical use cases for RNNs.mp456.12MB