Java, C, Machine learning, Matplotlib, Python, Gradient

Introduction to JAX (AI Adventures)

On May 15, 2020
@glouppe shared
RT @cdleary: Beautifully done video explaining JAX and some of its superpowers (jit, vmap, pmap): https://t.co/1eY9YnoPNM
Open

JAX is an open-source Python library that brings together Autograd and XLA, facilitating high-performance machine learning research. In this episode of AI Ad...

www.youtube.com
On May 15, 2020
@glouppe shared
RT @cdleary: Beautifully done video explaining JAX and some of its superpowers (jit, vmap, pmap): https://t.co/1eY9YnoPNM
Open

Introduction to JAX (AI Adventures)

Introduction to JAX (AI Adventures)

JAX is an open-source Python library that brings together Autograd and XLA, facilitating high-performance machine learning research. In this episode of AI Ad...

JAX: Autograd and XLA

JAX: Autograd and XLA

GPU- and TPU-backed NumPy with differentiation and JIT compilation. - google/jax

You don't know JAX

You don't know JAX

# Utility function for testing whether the net produces the correct # output for all possible inputs def test_all_inputs(inputs, params): predictions = [int(net(params, inp) > 0.5) for inp ...

Compiling machine learning programs via high-level tracing

Compiling machine learning programs via high-level tracing

Compiling machine learning programs via high-level tracing Roy Frostig∗ Google Brain [email protected] Matthew James Johnson∗ Google Brain [email protected] Chris Leary Google ...

Google Introduces Flax: A Neural Network Library for JAX

Google Introduces Flax: A Neural Network Library for JAX

Google recently introduce Flax — a neural network library for JAX that is designed for flexibility.

[DZone Research] Languages and Libraries for Artificial Intelligence

[DZone Research] Languages and Libraries for Artificial Intelligence

A discussion of the data collected as part of our 2018 Artificial Intelligence Guide survey, focusing on popular languages, libraries for AI development.

Data Science and Linear Algebra Fundamentals with Python, SciPy, & NumPy

Data Science and Linear Algebra Fundamentals with Python, SciPy, & NumPy

Learn to perform data science and linear algebra fundamentals using Python, Scipy, & NumPy.

Paperspace Gradient: A Modern PaaS for Machine Learning

Paperspace Gradient: A Modern PaaS for Machine Learning

This article is a part of the series where we explore cloud-based machine learning services. After covering Azure ML Services, Google Cloud ML Engine, and Amazon SageMaker, and IBM Watson ...

Run Deep learning models for free using google colaboratory

Run Deep learning models for free using google colaboratory

What is Google Colab: We all know that deep learning algorithms improve the accuracy of AI applications to great extent. But this accuracy comes with requiring…

Top 13 Python Deep Learning Libraries

Top 13 Python Deep Learning Libraries

Because of this, we’ve decided to start a series investigating the top Python libraries across several categories: Top 8 Python Machine Learning Libraries ✅ Top 13 Python Deep Learning ...

Click here to read the article

Click here to read the article

Typical use cases 10 engine %@% ' function getPartsOfSpeech(text) { var doc = new DocumentClass(text); var list = []; for (i = 0; i < doc.sentences().size(); i++) { var sentence = ...

JAX, aka NumPy on steroids

JAX, aka NumPy on steroids

JAX, a new research project by Google, has several features that make it interesting to a large audience. It looks like a NumPy wrapper, it makes efficiency one of its strengths, and it is ...

Steps to Learn Python for Data Science

Steps to Learn Python for Data Science

Data science is an interdisciplinary field of scientific processes, methods, and systems. It is used to extract insights from data in many forms, either struct…

General Guide To Learning Python For Data Analytics In 2019

General Guide To Learning Python For Data Analytics In 2019

Data Science and Python are two of the most common technical terms which we hear all everywhere. Combination of these two will give an advantage for the aspirants in the New Tech area.