Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update part2.qmd #7

Closed
wants to merge 1 commit into from
Closed

Update part2.qmd #7

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion Lessons/part2.qmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ In this course, we'll explore diffusion methods such as Denoising Diffusion Prob

Along the way, we'll cover essential deep learning topics like neural network architectures, data augmentation approaches, and various loss functions. We'll build our own models from scratch, such as Multi-Layer Perceptrons (MLPs), ResNets, and Unets, while experimenting with generative architectures like autoencoders and transformers.

Throughout the course, we'll PyTorch to implement our models, and will create our own deep learning framework called `miniai`. We'll master Python concepts like iterators, generators, and decorators to keep our code clean and efficient. We'll also explore deep learning optimizers like stochastic gradient descent (SGD) accelerated approaches, learning rate annealing, and learning how to experiment with the impact different initialisers, batch sizes and learning rates. And of course, we'll make use of handy tools like the Python debugger (pdb) and nbdev for building Python modules from Jupyter notebooks.
Throughout the course, we'll use PyTorch to implement our models, and will create our own deep learning framework called `miniai`. We'll master Python concepts like iterators, generators, and decorators to keep our code clean and efficient. We'll also explore deep learning optimizers like stochastic gradient descent (SGD) accelerated approaches, learning rate annealing, and learning how to experiment with the impact different initialisers, batch sizes and learning rates. And of course, we'll make use of handy tools like the Python debugger (pdb) and nbdev for building Python modules from Jupyter notebooks.

Lastly, we'll touch on fundamental concepts like tensors, calculus, and pseudo-random number generation to provide a solid foundation for our exploration. We'll apply these concepts to machine learning techniques like mean shift clustering and convolutional neural networks (CNNs), and will see how to use tracking with Weights and Biases (W&B).

Expand Down