| name: nbdev | |
| channels: | |
| - pytorch | |
| - fastai | |
| - huggingface | |
| - conda-forge | |
| - defaults | |
| dependencies: | |
| - python=3.10 | |
| - pandas |
| from fasthtml.common import * | |
| from fasthtml.oauth import HuggingFaceClient, redir_url | |
| # Auth client | |
| client = HuggingFaceClient(os.getenv("HF_CLIENT_ID"), os.getenv("HF_API_SECRET")) | |
| app = FastHTML() | |
| redir_path = "/auth/callback" | |
| # concepts/git_vs_http.md | |
| <!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | |
| rendered properly in your Markdown viewer. | |
| --> | |
| # Git vs HTTP paradigm | |
| The `huggingface_hub` library is a library for interacting with the Hugging Face Hub, which is a | |
| collections of git-based repositories (models, datasets or Spaces). There are two main |
This file contains the notebooks (from 01_matmul.ipynb to 14_augment.ipynb) developed in the Practical Deep Learning for Coders - part 2 of fast.ai's 2022-23 course.
This document explores matrix multiplication, starting from basic Python implementations and progressing to more advanced techniques using NumPy, PyTorch, and CUDA.
This is the summary of Practical Deep Learning for Coders - part 2 of fast.ai's 2022-23 course.
In this course, we’ll explore diffusion methods such as Denoising Diffusion Probabilistic Models (DDPM) and Denoising Diffusion Implicit Models (DDIM). We’ll get our hands dirty implementing unconditional and conditional diffusion models, experimenting with different samplers, and diving into recent tricks like textual inversion and Dreambooth.
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 use PyTorch to implement our models, and will create our own deep learning framework called miniai. We’ll master Python concepts like iterators, ge
Minai is a flexible and lightweight deep learning training framework developed interactively in the part 2 of fast.ai's 2022-23 course.
The core of minai is the Learner class, which orchestrates the training process through an extensive callback system, allowing users to easily modify or extend any part of the training loop.
Minai provides a set of utilities for data handling, device management, visualization, and performance optimization, making it suitable for both quick prototyping and advanced deep learning projects.