#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
https://github.com/BoltzmannEntropy/interviews.ai
GitHub
GitHub - BoltzmannEntropy/interviews.ai: It is my belief that you, the postgraduate students and job-seekers for whom the book…
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced research...
#python #deep_learning #flax #jax #language_model #large_language_models #natural_language_processing #transformer
https://github.com/young-geng/EasyLM
https://github.com/young-geng/EasyLM
GitHub
GitHub - young-geng/EasyLM: Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning…
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax. - young-geng/EasyLM
#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
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
GitHub
GitHub - jax-ml/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more - 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
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
GitHub
GitHub - huggingface/transformers: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models…
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - GitHub - huggingface/t...
#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
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
GitHub
GitHub - iree-org/iree: A retargetable MLIR-based machine learning compiler and runtime toolkit.
A retargetable MLIR-based machine learning compiler and runtime toolkit. - 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
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
GitHub
GitHub - google/flax: Flax is a neural network library for JAX that is designed for flexibility.
Flax is a neural network library for JAX that is designed for flexibility. - 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
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
GitHub
GitHub - lukasmasuch/best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. - lukasmasuch/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
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
GitHub
GitHub - huggingface/diffusers: 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch. - huggingface/diffusers