#jupyter_notebook #deep_learning #deep_reinforcement_learning #jax #machine_learning #numpy #reinforcement_learning #transformer
https://github.com/google/trax
https://github.com/google/trax
GitHub
GitHub - google/trax: Trax — Deep Learning with Clear Code and Speed
Trax — Deep Learning with Clear Code and Speed. Contribute to google/trax development by creating an account on GitHub.
#python #deep_learning #deep_neural_networks #jax #machine_learning #neural_networks
https://github.com/deepmind/dm-haiku
https://github.com/deepmind/dm-haiku
GitHub
GitHub - google-deepmind/dm-haiku: JAX-based neural network library
JAX-based neural network library. Contribute to google-deepmind/dm-haiku development by creating an account on GitHub.
#python #convolutional_neural_networks #deep_learning #imagenet #jax #pytorch #tensorflow2 #transfer_learning
https://github.com/google-research/big_transfer
https://github.com/google-research/big_transfer
GitHub
GitHub - google-research/big_transfer: Official repository for the "Big Transfer (BiT): General Visual Representation Learning"…
Official repository for the "Big Transfer (BiT): General Visual Representation Learning" paper. - google-research/big_transfer
#java #dropwizard #hibernate #jax_rs #jersey2 #jetty #rest #web_framework
https://github.com/dropwizard/dropwizard
https://github.com/dropwizard/dropwizard
GitHub
GitHub - dropwizard/dropwizard: A damn simple library for building production-ready RESTful web services.
A damn simple library for building production-ready RESTful web services. - dropwizard/dropwizard
#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