4 repository-uri
Awesome GitHub RepositoriesGenerative Model Development
The design and training of models that synthesize new data, such as GANs, VAEs, and neural radiance fields.
Distinct from Variational Autoencoders: Existing candidates are limited to specific architectures like VAEs or NeRFs rather than the general field of generative modeling.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Generative Model Development. Refine with filters or upvote what's useful.
Awesome Generative Model Development GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- cs231n/cs231n.github.io
cs231n/cs231n.github.io
10,923Vezi pe GitHubThis project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum
Includes instructional content on creating realistic high-dimensional data using GANs, variational autoencoders, and neural radiance fields.
Jupyter Notebook - czy36mengfei/tensorflow2_tutorials_chinese
czy36mengfei/tensorflow2_tutorials_chinese
7,786Vezi pe GitHubThis project is a collection of educational resources and instructional guides for learning deep learning and neural network implementation using TensorFlow. It provides a structured set of tutorials and notebooks written in Chinese, covering supervised and unsupervised learning tasks. The material focuses on practical implementations of diverse neural network architectures, including convolutional, recurrent, and autoencoder networks. It includes specific training content for computer vision, natural language processing, and generative models. The coverage extends to specialized network arc
Provides guides for developing generative models, including autoencoders and GANs.
Jupyter Notebook - openai/consistency_models
openai/consistency_models
6,492Vezi pe GitHubThis project is a framework for training and sampling generative models designed to produce high-quality images in few steps. It provides implementations for image generation models that transform random noise into structured visual data through an optimized sampling process. The system specializes in accelerating image generation through consistency distillation and consistency training. It includes tools to transform pre-trained diffusion models into faster versions by distilling knowledge from a teacher model into a student model, as well as methods to train consistency models from scratch
Facilitates the development of high-speed generative models through distillation and consistency training.
Python - rasbt/python-machine-learning-book-3rd-edition
rasbt/python-machine-learning-book-3rd-edition
4,988Vezi pe GitHubThis is the companion code repository for the third edition of the book Python Machine Learning. It delivers the entire learning path as a structured collection of Jupyter notebooks that progress from classical machine learning algorithms to advanced deep learning models, with every concept demonstrated through executable code and narrative text. What distinguishes this resource is its pedagogical design. Each notebook cell encapsulates a single conceptual step, letting readers run, inspect, and modify discrete units of learning. The code provides interchangeable implementations of deep lea
Builds cutting-edge models such as generative adversarial networks and reinforcement learning agents.
Jupyter Notebookdeep-learningmachine-learningscikit-learn
