#jupyter_notebook #deep_learning #deep_reinforcement_learning #jax #machine_learning #numpy #reinforcement_learning #transformer

https://github.com/google/trax

#python #deep_learning #deep_neural_networks #jax #machine_learning #neural_networks

https://github.com/deepmind/dm-haiku

#python #convolutional_neural_networks #deep_learning #imagenet #jax #pytorch #tensorflow2 #transfer_learning

https://github.com/google-research/big_transfer

#java #dropwizard #hibernate #jax_rs #jersey2 #jetty #rest #web_framework

https://github.com/dropwizard/dropwizard

#java #http_client #interface #jax_rs #okhttp3 #slf4j

https://github.com/OpenFeign/feign

#other #artificial_intelligence #autograd #bayesian_statistics #convolutional_neural_networks #data_science #deep_learning #ensemble_learning #feature_extraction #graduate_school #information_theory #interview_preparation #jax #jobs #logistic_regression #loss_functions #machine_learning #python #pytorch #pytorch_tutorial

https://github.com/BoltzmannEntropy/interviews.ai

#python #jax #monte_carlo_tree_search #reinforcement_learning

https://github.com/deepmind/mctx

#python #deep_learning #flax #jax #language_model #large_language_models #natural_language_processing #transformer

https://github.com/young-geng/EasyLM

#python #autodiff #interval_arithmetic #jax

https://github.com/google/autobound

#python #jax

JAX is a Python library that helps you do fast and efficient numerical computing, especially for machine learning. It can automatically find the derivatives of functions, which is useful for training neural networks. JAX also compiles your code to run on GPUs and TPUs, making it much faster. You can use functions like `grad` for differentiation, `jit` for compilation, `vmap` for vectorization, and `pmap` for parallel computing across multiple devices.

Using JAX benefits you by speeding up your computations, allowing you to handle large datasets and complex algorithms more efficiently. It also makes it easier to write and optimize your code without leaving Python. This means you can focus on your research or projects without worrying about the underlying performance details.

https://github.com/jax-ml/jax

#python #bert #deep_learning #flax #hacktoberfest #jax #language_model #language_models #machine_learning #model_hub #natural_language_processing #nlp #nlp_library #pretrained_models #python #pytorch #pytorch_transformers #seq2seq #speech_recognition #tensorflow #transformer

The Hugging Face Transformers library provides thousands of pretrained models for various tasks like text, image, and audio processing. These models can be used for tasks such as text classification, image detection, speech recognition, and more. The library supports popular deep learning frameworks like JAX, PyTorch, and TensorFlow, making it easy to switch between them.

The benefit to the user is that you can quickly download and use these pretrained models with just a few lines of code, saving time and computational resources. You can also fine-tune these models on your own datasets and share them with the community. Additionally, the library offers a simple `pipeline` API for immediate use on different inputs, making it user-friendly for both researchers and practitioners. This helps in reducing compute costs and carbon footprint while enabling high-performance results across various machine learning tasks.

https://github.com/huggingface/transformers

#cplusplus #compiler #cuda #jax #machine_learning #mlir #pytorch #runtime #spirv #tensorflow #vulkan

IREE is a tool that helps run Machine Learning (ML) models on different devices, from big data centers to small mobile and edge devices. It uses a special way to convert ML models into a uniform format, making it easier to deploy them anywhere. This tool is still in the early stages but is being actively improved. Using IREE can help you scale your ML models efficiently across various platforms, making it beneficial for developers who need to deploy models in different environments.

https://github.com/iree-org/iree

#jupyter_notebook #jax

Flax is a library for creating neural networks with JAX. It offers a flexible way to build and analyze these networks. The new Flax NNX API makes it easier to work with neural networks by using regular Python objects, which helps in creating, debugging, and analyzing models more efficiently. This means users can express their models in a more intuitive way, making it simpler to develop and modify neural networks. Flax also provides many tools and examples to help users get started quickly.

https://github.com/google/flax

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

https://github.com/ml-tooling/best-of-ml-python

#python #deep_learning #diffusion #flax #flux #hacktoberfest #image_generation #image2image #image2video #jax #latent_diffusion_models #pytorch #score_based_generative_modeling #stable_diffusion #stable_diffusion_diffusers #text2image #text2video #video2video

The Hugging Face Diffusers library is a powerful and easy-to-use tool for generating images, audio, and 3D molecular structures using advanced diffusion models. It offers ready-to-use pretrained models and flexible components like pipelines, schedulers, and model building blocks, allowing you to quickly create or customize your own diffusion-based projects. Installation is simple via pip or conda, and you can generate high-quality outputs with just a few lines of code. This library benefits you by making cutting-edge AI generation accessible, customizable, and efficient, whether you want to run models or train your own[1][2][5].

https://github.com/huggingface/diffusers