sololearner9 Goto Github PK

netwrkx

✿☃ Networking and Security

neuroscience-ai-reading-course

Notes for the Neuroscience & AI Reading Course (SEM-I 2020-21) at BITS Pilani Goa Campus

numpy

Numpy main repository

nvae

The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder"

openmct

A web based mission control framework.

pandas_exercises

Practice your pandas skills!

paper-a-week

papers-literature-ml-dl-rl-ai

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

parlai

A framework for training and evaluating AI models on a variety of openly available dialog datasets.

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

research-internships-for-undergraduates

List of Research Internships for Undergraduate Students

roboticspenn

Coursera Specialization: Robotics 🛰| UPenn

seaborn

Statistical data visualization using matplotlib

selfdrive

⛊ Self Driving Car with Deep Learning

selfkdrive

🚓 ML based self-driving RC car 🚥

spacexland

🗽🤖 ✈ Landing a Virtual SpaceX rocket

stanalgo

Alg🔘rithms | Design Alg🔘rithms

stanford-cs-221-artificial-intelligence

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

stanford-cs229

These are my solutions to the problem sets for Stanford's Machine Learning class - cs229

stanfordai

🚀 Stanford | CS #AI👾

stanfordss

🗼🛫 Stanford Summer☀/ Silicon Valley | Visiting Student (Summer Session)

stansystems

🀄📡 Systems, Unix, Architecture and HPC

summer-internships

A document to help undergraduates keep track of Software Engineering Internship opportunities as well as Research Internships.

tensorflow

An Open Source Machine Learning Framework for Everyone

tensorflow-examples

TensorFlow Tutorial and Examples for beginners

the-mechanics-of-machine-learning

The Mechanics of Machine Learning Book contents Work in progress Book version 0.4 Terence Parr and Jeremy Howard Copyright © 2018-2019 Terence Parr. All rights reserved. Please don't replicate on web or redistribute in any way. This book generated from markup+markdown+python+latex source with Bookish. You can make comments or annotate this page by going to the annotated version of this page. You'll see existing annotated bits highlighted in yellow. They are PUBLICLY VISIBLE. Or, you can send comments, suggestions, or fixes directly to Terence. Warning: The content of this book is so unexciting that you'll be able to use it in your actual job! This book is a primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. We focus on just a few powerful models (algorithms) that are extremely effective on real problems, rather than presenting a broad survey of machine learning algorithms as many books do. Co-author Jeremy used these few models to become the #1 competitor for two consecutive years at Kaggle.com. This narrow approach leaves lots of room to cover the models, training, and testing in detail, with intuitive descriptions and full code implementations. This is a book in progress and we will add chapters and make edits

tools-to-design-or-visualize-architecture-of-neural-network

Tools to Design or Visualize Architecture of Neural Network

vmls-companions

These are companion notebooks written in Julia and Python for: "Introduction to Applied Linear Algebra" by Boyd and Vandenberghe.