hailu21 Goto Github PK

18303

18.303 - Linear PDEs course

365-days-computer-vision-learning-linkedin-post

365 Days Computer Vision Learning Linkedin Post

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

6s083

Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic

appliedml

approachingalmost

Approaching (Almost) Any Machine Learning Problem

apscheduler

Task scheduling library for Python

begin_python

Beginning Python programming course using the Temperature Control Lab.

bigquery_helper

Helper class to simplify common read-only BigQuery tasks.

bqplot

Plotting library for IPython/Jupyter notebooks

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

classification

Classification Algorithms

cookbook-2nd-code

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

copilot.vim

Neovim plugin for GitHub Copilot

covid-net

COVID-Net Open Source Initiative

dash

Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.

data-engineering-zoomcamp

Code for Data Engineer Zoomcamp course

data_science

Python Data Science Course with TCLab

deep-learning-resources

A curated list of deep learning resources books, courses, papers, libraries, conferences, sample code, and many more.

free-python-books

Python books free to read online or download

gekko

GEKKO Python for Machine Learning and Dynamic Optimization

hailu21

Config files for my GitHub profile.

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

ilearndeeplearning.py

This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well as to check how the code works in the action.

image-classification-fastai.jl

Getting started with Julia Machine Learning Library with FastAI.jl

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

leaf-disease-detection

Deep Learning Model I made using PyTorch for Cassava Leaf Disease Classification

learn-keras-for-deep-neural-networks

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

learnr

Interactive Tutorials with R Markdown

lintr

Static Code Analysis for R