Auto Differentiation This is really powerful to automate the calculation of derivatives especially when differentiating complex algorithms and mathematical functions. It is highly useful in training machine learning models and can be done with 'jax.grad()'
@sumanth_077
-
JAX NumPy: Performing NumPy Operations with JAX Library
By
–
You can perform almost all Numpy Operations with Jax. JAX provides 'jax.numpy' that helps you to perform Almost anything that can be done with 'numpy' Here is how you can use "jax.numpy"
-
JAX vs NumPy: GPU and TPU Support Comparison
By
–
We have Numpy which is the fundamental python library for scientific computing. Why do we need Jax or How it differs from Numpy Numpy is only compatible with the CPU. JAX provides an implementation of NumPy with both GPU and TPU support. https://
github.com/google/jax -
Jax: A Faster Python Library for Machine Learning and Computing
By
–
"Jax" is a python library for numerical computing, machine learning and is really faster than Numpy Here is why you should care about Jax and its really powerful features in Machine Learning:
-
Implementing ML Projects: Learning from Production Approaches
By
–
If you are looking to implement your ML project? Learn how other organizations approached the same machine learning in production. "Applied ML" has papers, articles, and blogs on data science & machine learning that helps you with that: Check this: http://
github.com/eugeneyan/appl
ied-ml
… -
Hugging Face PEFT Project: Open Source Repository and Blog Overview
By
–
This Project from Hugging Face is completely Open Source, Check out the repo here: https://
github.com/huggingface/pe
ft
… Consider checking this blog from Hugging Face Team for more detailed overview. https://
huggingface.co/blog/peft 5/5 -
That’s a Wrap: Daily Python Data Science Machine Learning Tutorials
By
–
That's a wrap! Everyday, I share tutorials around Python, Data Science & Machine Learning. You can follow me → @Sumanth_077 Like/RT the first tweet to support my work and help this reach more people.
-
Getting Started with PEFT: Simple 3-Step Setup Guide
By
–
Finally Getting started with PEFT is really simple: 1. Import the Necessary Libraries
2. Define the Config with PEFT method
3. Wrapping base model from Hugging Face Transformers by calling `get_peft_model` That's it. You can start training now. Check the below code 4/5 -
Interesting Use Cases of PEFT Parameter Efficient Fine-Tuning
By
–
Some of the interesting usecases of PEFT are: 1. Stable Diffusion Dreambooth training.
2. Finetuning the `bigscience/T0_3B` model which has around 3 Billion Parameters. Checkout them here: https://
github.com/huggingface/pe
ft#use-cases
… 3/5 -
PEFT: Fine-tuning Large Models on Low-End Hardware
By
–
But fine-tuning large models on low-end hardware is a real challenge PEFT solves this by fine-tuning a small number of model parameters while freezing most parameters of the pre-trained LLMs. This reduces the computational and storage costs 2/5 https://
github.com/huggingface/pe
ft
…