A latent text-to-image diffusion model

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

The fastai deep learning library

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

This repository contains the source code for the paper First Order Motion Model for Image Animation

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Using Low-rank adaptation to quickly fine-tune diffusion models.

T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

Silero Models: pre-trained text-to-speech models made embarrassingly simple

Efficient Image Captioning code in Torch, runs on GPU

The 3rd edition of course.fast.ai

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning

Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference?

Try out deep learning models online on Google Colab

Generate images from texts. In Russian

Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.

A smaller subset of 10 easily classified classes from Imagenet, and a little more French

Modern Computer Vision with PyTorch, published by Packt

Reference code for "Motion-supervised Co-Part Segmentation" paper

Temporary home for fastai v2 while it's being developed

Nomeroff Net. Automatic numberplate recognition system.

The materials for the course MTH 594 Advanced data mining: theory and applications (Dmitry Efimov, American University of Sharjah)

Приёмы в машинном обучении

Code for the paper "Controlling Style and Semantics in Weakly-Supervised Image Generation", ECCV 2020

Приёмы при написании python-программ