├── README.md ├── Week 1 ├── 1_D tensors exercise.py ├── 2_D Tensors exercise.py ├── 2_D Tensors.py ├── Dataset Exercise.py ├── Dataset and imgs.py ├── Differentiation.py ├── Prebuilt Dataset Exercise.py ├── Simple_dataset.py ├── Tensors 1 D Overview.py ├── img.tar.gz └── index.csv ├── Week 2 ├── LR with DataLoader and Optimizers Train and Val.py ├── LR with DataLoader and Optimizers.py ├── Linear Regression 1D Stochastic Gradient Descent and DataLoader.py ├── MiniBatch Gradient Descent.py ├── Simple Linear Regression 1D Training One Parameter.py ├── Simple Linear Regression.py └── Simple Linear regression 1D Training Two Parameter.py ├── Week 3 ├── Logistic Regression Training (Cross-Entropy).py ├── Logistic Regression and Bad Initialization Value.py ├── Logistic Regression.py ├── Multiple Linear Regression Training.py ├── Multiple Linear Regression with Multiple Outputs training.py └── Multiple Linear Regression.py ├── Week 4 ├── Activation Functions.py ├── Neural Networks More Hidden Neutrons.py ├── Neural Networks with One Hidden Layer1.py ├── Neural Networks with one Hidden Layer.py ├── Simple One Hidden Layer Neural Network.py ├── Softmax 1D.py ├── Softmax MNIST Classifier.py └── Test Activation Functions on the MNIST Dataset.py ├── Week 5 ├── Batch Normalization with MNIST Dataset.py ├── Creating a Neural Network with nn.Modulelist.py ├── Deep Network Sigmoid, Tanh and Relu on MNIST Dataset.py ├── Dropout in Classification.py ├── Dropout in Regression.py ├── Initialization with same weights experiment.py ├── Neural Networks with Momentum.py └── Weights Experiment_Uniform_He_Xavier_Identical_Default.py ├── Week 6 ├── Convolution Multiple Inputs and Outputs.py ├── Convolution Network Simple.py ├── Convolution.py ├── Convolutional Neural Network with Small Images.py └── MNIST CNN BatchNorm vs Normal.py └── Week 7 ├── FashionMNIST Project Honors.py ├── First 3 Items.png ├── Image1 Validation_set.png ├── Image2 Validation_set.png ├── Image3 Validation_set.png 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