Need for compute accelerators in a Deep Learning course
36:22
Need for compute accelerators in a Deep Learning course
Introduction to (Deep) Reinforcement Learning
1:06:14
Introduction to (Deep) Reinforcement Learning
Unsupervised Deep Learning
1:09:33
Unsupervised Deep Learning
Autoencoder
47:34
Autoencoder
Generative Adversarial Networks (GANs) and beyond
1:21:33
Generative Adversarial Networks (GANs) and beyond
Image segmentation and U-net
33:40
Image segmentation and U-net
Introduction to Generative Adversarial Network (GANs)
25:44
Introduction to Generative Adversarial Network (GANs)
CNNs -- overview
22:10
CNNs -- overview
Transfer learning
17:08
Transfer learning
CNN visualization
14:03
CNN visualization
Convolution Neural Networks with PyTorch
21:02
Convolution Neural Networks with PyTorch
Neural Networks with PyTorch
30:48
Neural Networks with PyTorch
Introduction to PyTorch
1:05:42
Introduction to PyTorch
Residual Networks recap
38:48
Residual Networks recap
CNN architectures
20:23
CNN architectures
Regularization, Batch Normalization, Dropouts and others to improve neural network training
14:55
Regularization, Batch Normalization, Dropouts and others to improve neural network training
Dropouts in Deep Neural Network Training
17:16
Dropouts in Deep Neural Network Training
Batch normalization in Deep Neural Network Training
16:06
Batch normalization in Deep Neural Network Training
Reinforcement Learning principles, the Markov Decision Process, definitions, codes
1:12:05
Reinforcement Learning principles, the Markov Decision Process, definitions, codes
Visual Studio Code (VSCode) Setup in Windows 10 for C/C++ Programming
9:12
Visual Studio Code (VSCode) Setup in Windows 10 for C/C++ Programming
Visual Studio Code (VSCode) setup in MacOS for C/C++ Programming
3:10
Visual Studio Code (VSCode) setup in MacOS for C/C++ Programming
Recurrent Neural Network: Long Short-term Memory (LSTM) architecture
44:17
Recurrent Neural Network: Long Short-term Memory (LSTM) architecture
Recurrent Neural Network : The vanilla architecture
58:49
Recurrent Neural Network : The vanilla architecture
A basic Language modeling with Recurrent Neural Network
29:15
A basic Language modeling with Recurrent Neural Network
Introduction to Recurrent Neural Network
38:20
Introduction to Recurrent Neural Network
Convolution Neural Network: an Epilog with a note on the weight sharing property
9:02
Convolution Neural Network: an Epilog with a note on the weight sharing property
Convolution Neural Networks: Few examples
14:16
Convolution Neural Networks: Few examples
Convolution Neural Network: The Flattened layer and FullyConnected Layers
14:08
Convolution Neural Network: The Flattened layer and FullyConnected Layers
Convolution Neural Network: The Pooling layer
11:11
Convolution Neural Network: The Pooling layer
CNN 2 Convolution operation+1x1Conv+Implementation
1:27:10
CNN 2 Convolution operation+1x1Conv+Implementation