Lesson 1 - PyTorch Basics and Gradient Descent
Deep Learning with PyTorch: Zero to GANs
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Course HomeAssignment 1 - All About torch.TensorLesson 2 - Working with Images and Logistic RegressionAssignment 2 - Train Your First ModelLesson 3 - Training Deep Neural Networks on a GPUAssignment 3 - Feed Forward Neural NetworksLesson 4 - Image Classification with Convolutional Neural NetworksLesson 5 - Data Augmentation, Regularization & ResNetsLesson 6: Generative Adversarial Networks and Transfer LearningProject - Train a Deep Learning Model from Scratch
Hindi version: https://youtu.be/7yRkvPxE7Hs
We start this lesson with the basics of PyTorch: tensors, gradients, and Autograd. We then proceed to implement linear regression and gradient descent from scratch, first using matrix operations and then using PyTorch built-ins. Notebooks used in this lesson:
- PyTorch Basics: jovian.ai/aakashns/01-pytorch-basics
- Linear Regression: jovian.ai/aakashns/02-linear-regression
- Machine Learning: jovian.ai/aakashns/machine-learning-intro
Ask questions on the course discussion forum.
aakashns/01-pytorch-basics
aakashns/02-linear-regression
aakashns/machine-learning-intro
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