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Introduction
2. The number one rule of ML
6. What can deep learning be used for?
10. How to (and how not to) approach this course
14. Creating tensors
20. Matrix multiplication
27. Selecting data (indexing)
30. Accessing a GPU
35. Creating a dataset with linear regression
41. Checking out the internals of our model
45. PyTorch training loop intuition
54. Putting everything together
64. Turing our data into tensors
69. Loss, optimizer and evaluation functions for classification
76. Creating a straight line dataset
88. Troubleshooting a mutli-class model
95. TorchVision
103. Training and testing loops for batched data
112. Convolutional neural networks (overview)
118. Training our first CNN
126. Introduction to custom datasets
136. Creating image DataLoaders
143. Data augmentation
151. Plotting model 0 loss curves
157. Predicting on custom data
Hello :)
2. The number one rule of ML
6. What can deep learning be used for?
10. How to (and how not to) approach this course
14. Creating tensors
20. Matrix multiplication
27. Selecting data (indexing)
30. Accessing a GPU
35. Creating a dataset with linear regression
41. Checking out the internals of our model
45. PyTorch training loop intuition
54. Putting everything together
64. Turing our data into tensors
69. Loss, optimizer and evaluation functions for classification
76. Creating a straight line dataset
88. Troubleshooting a mutli-class model
95. TorchVision
103. Training and testing loops for batched data
112. Convolutional neural networks (overview)
118. Training our first CNN
126. Introduction to custom datasets
136. Creating image DataLoaders
143. Data augmentation
151. Plotting model 0 loss curves
157. Predicting on custom data
Intro
1 Installation
2 Tensor Basics
3 Autograd
4 Backpropagation
5 Gradient Descent
6 Training Pipeline
7 Linear Regression
8 Logistic Regression
9 Dataset and Dataloader
10 Dataset Transforms
11 Softmax and Crossentropy
12 Activation Functions
13 Feed Forward Net
14 CNN
15 Transfer Learning
16 Tensorboard
17 Save & Load Models
Introduction
PyTorch Basics & Linear Regression
Image Classification with Logistic Regression
Training Deep Neural Networks on a GPU with PyTorch
Image Classification using Convolutional Neural Networks
Residual Networks, Data Augmentation and Regularization
Training Generative Adverserial Networks (GANs)