#Generative Adversarial Networks

Explore the MIT Deep Learning repository, which features a well-rounded set of tutorials focused on neural network basics, driving scene segmentation, and advanced techniques like generative adversarial networks. The DeepTraffic competition further enriches your learning experience by offering practical challenges in deep reinforcement learning. This evolving resource, aligned with MIT's ongoing courses, serves as a beneficial tool for newcomers and experienced practitioners in artificial intelligence.

Discover a broad array of Keras-based Generative Adversarial Network (GAN) implementations that simplify research paper models to highlight fundamental concepts. This repository hosts diverse GAN models like AC-GAN, CycleGAN, and DCGAN, prioritizing the essence of each over intricate layer setups. Contributions and model suggestions are welcomed. Included are all codes needed for easy installation and execution, utilizing Keras for practical deep learning applications. Note: Collaboration opportunities available for ongoing development.

Delve into the second edition official codebase of O'Reilly's Generative Deep Learning book to discover methods for automating creative processes in machines. Learn about key models such as Variational Autoencoders and Generative Adversarial Networks and implement them using practical tutorials with Kaggle datasets and Docker. The guide features integration with Tensorboard for real-time monitoring and utilizes Keras resources, adaptable for local or cloud computing environments.

TF-GAN is a versatile library that simplifies the process of training and evaluating Generative Adversarial Networks (GANs). Easily installable via pip, it offers seamless integration with existing workflows through TF-GAN calls, custom scripts, or other frameworks. Its modular components include Core for foundational training support, Features for standard GAN operations, Losses such as Wasserstein, and Evaluation metrics like Inception Score and Frechet Distance. TF-GAN is utilized in various Google projects and supports numerous research initiatives. It accommodates different GAN configurations and offers flexibility in model training, making it accessible for a broad audience, from academic researchers to industry professionals.

Discover a set of TensorFlow tutorials created specifically for beginners with clear examples for both TF v1 and v2. Learn traditional and modern practices, such as layers, estimators, and dataset APIs. The project includes instructions on basic operations, linear and logistic regressions, word embedding models, gradient boosting, and various neural network architectures. Additional guides cover data management, multi-GPU training, and customized layers. Keep up-to-date with the latest TensorFlow methods and enhance your skills with practical, hands-on experiences.

This repository provides concise tutorial codes for deep learning researchers to learn PyTorch efficiently, with models mostly under 30 lines of code. It presents a coherent learning trajectory from foundational topics like PyTorch basics, linear regression, to advanced subjects such as Generative Adversarial Networks and Neural Style Transfer. This guide serves as a practical supplement to the Official PyTorch Tutorial, offering vital resources for developing proficiency in PyTorch. The prerequisites include Python 2.7 or 3.5+ and PyTorch 0.4.0+.

This open-source project leverages neural networks to turn grayscale photos into color images, featuring step-by-step tutorials from basic neural models to complex GAN architectures. With insights into color space conversion, this project also explores efficient image resolutions and pretrained model optimizations, offering developers and researchers a comprehensive resource for mastering AI-driven image colorization.

Delve into a comprehensive collection of GAN inversion resources encompassing diverse methods and applications in both 2D and 3D contexts. Published in TPAMI 2022, this survey highlights key academic works and technical implementations, including GAN latent space editing and real-world uses like image generation and facial recognition. Investigate inversion and editing approaches in both conventional GANs and advanced diffusion models, with direct access to related projects, academic papers, and source code.

iGAN leverages deep learning techniques, such as GANs and DCGANs, for interactive image generation, producing realistic images with minimal strokes. It functions as a dual-purpose platform: a drawing interface and a tool for visual understanding of deep generative models. The platform supports real-time processing, compatibility with Python 2 and 3, and uses libraries like Theano and OpenCV with GPU acceleration, offering robust editing and visualization features suitable for varied artistic and analytic tasks.

This collection gathers a wide range of papers and code concerning Generative Adversarial Networks (GANs), offering insights into applications such as image translation and facial attribute manipulation. It also delves into theoretical perspectives and machine learning methods with interdisciplinary applications in fields like medicine and music. Featuring advancements in autoML, image animation, and GAN theory, this repository serves researchers and developers interested in exploring GAN technology comprehensively. This resource ensures an extensive understanding of GANs' impact across varied domains.

Grenade is a dependently typed neural network library optimized for Haskell that allows for precise configuration of complex networks. It supports both recurrent and convolutional networks, achieving low error rates on tasks like MNIST. Through its flexible layer structure, Grenade offers support for custom network designs including residual and series parallel graphs. The library is purely functional and efficient, utilizing backpropagation and gradient updates with performance optimized by C-based LAPACK and BLAS libraries.

This repository provides detailed PyTorch deep learning tutorials aligned with the Udacity Deep Learning Nanodegree, featuring projects such as bike-sharing predictions and dog breed classification. It also includes topics like CNNs, RNNs, GANs, as well as weight initialization and batch normalization. Additionally, learn to deploy models with AWS SageMaker for a comprehensive learning experience.

Discover a detailed set of annotated PyTorch implementations focused on neural networks and deep learning algorithms. The resource is continually updated and documented with comprehensible notes, providing practical insights into models like Transformers, GANs, and Diffusion Models, as well as reinforcement learning methods. Suitable for developers interested in architectures and optimization strategies, and complemented by regular updates to ensure resourcefulness. An essential repository for those wishing to broaden their deep learning acumen.

This innovative project leverages generative adversarial networks to create multi-track polyphonic music, either from scratch or by enhancing existing tracks. Utilizing the Lakh Pianoroll Dataset for training, it offers a streamlined process with 3D convolutional layers for managing temporal structures. The platform supports workflows including training, inference, and interpolation, making it a valuable resource for musicians and developers.