hakrosabir

hakrosabir's Stars

• microsoft/AI-For-Beginners

  12 Weeks, 24 Lessons, AI for All!

  Language:Jupyter Notebook35.2k6k

• pjreddie/darknet

  Convolutional Neural Networks

  Language:C25.9k21.3k

• Kozea/pygal.js

  pygal javascript modules

  Language:JavaScript156

• 83au/Nicole-s-3D-Virtual-Tours

  Nicole's 3D Virtual Tours website

  Language:JavaScript3

• hakrosabir/Face_And_Emotion_Detection

  Performing image classification for detection of various human emotions using CNN Architecture.

  1

• hakrosabir/My-Projects-Completed-with-Deep-Learning-Nanodeh

  1

• opencv/opencv

  Open Source Computer Vision Library

  Language:C++79.6k55.9k

• tensorflow/tensorflow

  An Open Source Machine Learning Framework for Everyone

  Language:C++187k74.4k

• hakrosabir/introtodeeplearning

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

  1

• aamini/introtodeeplearning

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

  Language:Jupyter Notebook7.3k3.7k

• FrontendMasters/gatsby-intro

  Code for the Introduction to Gatsby course.

  Language:JavaScript253234

• btholt/complete-intro-to-react-v5

  The Complete Intro to React, the fifth version

  Language:JavaScript3.6k1.1k

• hakrosabir/commercewithnextjs

  Language:TypeScript1

• hakrosabir/porfoliomadewithgatsbyjs

  Playful and Colorful One-Page portfolio featuring Parallax effects and animations. Especially designers and/or photographers will love this theme! Built with MDX and Theme UI.

  Language:JavaScript1

• hakrosabir/practicalDL

  A Practical Guide to Deep Learning with TensorFlow 2.0 and Keras materials for Frontend Masters course

  1

• facebookresearch/svoice

  We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

  Language:Python1.3k184

• MuhammadMohsin/PanacloudBootcamp2020

  Language:HTML10786

• panacloud/bootcamp-2020

  Learn to Build Modern Full Stack Serverless Multi-Tenant SaaS Apps and APIs

  486180

• hakrosabir/Frontend-Cheat-Sheets

  Collection of cheat sheets(HTML, CSS, JS, Git, Gulp, etc.,) for your frontend development needs & reference

  1

• NirantK/awesome-project-ideas

  Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

  8k1.2k

• jonkrohn/ML-foundations

  Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

  Language:Jupyter Notebook3.6k1.7k

• DataForScience/DeepLearning

  Deep Learning From Scratch

  Language:Jupyter Notebook137108

• hakrosabir/DataViz

  Data Visualization With Matplotlib and Seaborn

  1

• rapPayne/flutter-intro-presentation

  Files to support Rap's Intro to Google Flutter Presentation

  Language:C++1313

• hakrosabir/Deep_Learning_AI

  1

• marcopeix/Deep_Learning_AI

  Language:Jupyter Notebook131158

• Kulbear/deep-learning-nano-foundation

  Udacity's Deep Learning Nano Foundation program.

  Language:Jupyter Notebook393151

• fastai/fastbook

  The fastai book, published as Jupyter Notebooks

  Language:Jupyter Notebook22.1k8.5k

• jcjohnson/cnn-benchmarks

  Benchmarks for popular CNN models

  Language:Python2.5k408

• cs231n/gcloud

  Google Cloud tutorial and setup

  Language:Python488184