Week 1 - Tensor and Datasets
1.1 - Tensors 1D
1.2 - Two-Dimensional Tensors
1.3 - Derivatives in PyTorch
1.4 - Simple Dataset
1.5 - Dataset
Week 2 - Linear Regression, Linear Regression PyTorch Way
2.1 - Linear Regression in 1D - Prediction
2.2 - Linear Regression Training
2.3 - Gradient Descent and Cost
2.4 - PyTorch Slope
2.5 - Linear Regression Training
***** Linear Regression PyTorch Way *****
3.1 - Stochastic Gradient Descent and the Data Loader
3.2 - Mini-Batch Gradient Descent
3.3 - Optimization in Pytorch
3.4 - Training, Validation and Test Split
Week 3 - Multiple Input Output Linear Regression, Logistic Regression for Classification
4.1 - Multiple Linear Regression Prediction
4.2 - Multiple Output Linear Regression
***** Logistic Regression for Classification *****
5.0 - Linear Classifier and Logistic Regression
5.1 - Logistic Regression Prediction
5.2 - Bernoulli Distribution Maximum Likelihood Estimation
5.3 - logistic Regression Cross Entropy Loss
Week 4 - Softmax Regression, Shallow Neural Networks
6.1 - Softmax Prediction
6.2 - Softmax Function
6.3 - Softmax PyTorch
***** Shallow Neural Networks
7.1 - Neural Networks in One Dimension
7.2 - Neural Networks More Hidden Neurons
7.3 - Neural Networks with Multiple Dimensional Input
7.4 - Multi-Class Neural Networks
7.5 - Backpropagation
7.6 - Avtivation Functions
Week 5 - Deep Networks
8.1 - Deep Neural Networks
8.2 - Dropout
8.3 - Neural Network initialization weights
8.4 - Gradient Descent with Momentum
8.5* - Batch Normalization
Week 6 - Convolutional Neural Network
9.1 - Convolution
9.2 - Activation Functions and Max Polling
9.3 - Multiple Input and Output Channels
9.4 - Convolutional Neural Network
9.5 - Torch-Vision Models
Week 7 - Peer Review
- Fashion MNIST Classification Assignment