emezac

100-Days-Of-ML-Code

100 Days of ML Coding

360-video-player-for-android

Sample code for creating a 360 video player on Android. Creates an OpenGL ES scene that renders a sphere textured with video frames from MediaPlayer playback. Does not use the NDK. This is a simple example of the core elements of 360 video rendering and is not intended to be production quality code. The code may be unstable. This is won't be maintained.

connectfourchaellenge

deep-image-prior

Image restoration with neural networks but without learning.

deep-learning

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-painterly-harmonization

Code and data for paper "Deep Painterly Harmonization": https://arxiv.org/abs/1804.03189

deep-photo-styletransfer

Code and data for paper "Deep Photo Style Transfer": https://arxiv.org/abs/1703.07511

GREact

A React JS app which displays similar words, especially useful for GRE exam, grouped together under a common topic along with their meanings and examples for easy recollection by the learner

unicode

Unicode normalization library. (Mirror of Yoshida-san's code base to maintain the RubyGem.)

emezac/d2l-en

Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook. With code, math, and discussions.

emezac/deepnude_official

emezac/DonManolo

emezac/face_recognition

The world's simplest facial recognition api for Python and the command line

emezac/FFCVSR

AAAI 2019: Frame and Feature-Context Video Super-Resolution

emezac/Gated-GAN

Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer

emezac/Grokking-Deep-Learning

this repository accompanies my forthcoming book "Grokking Deep Learning"

emezac/interviews

Everything you need to know to get the job.

emezac/jupyter

Jupyter metapackage for installation, docs and chat

emezac/kaggle-web-traffic

1st place solution

emezac/labelImg

:metal: LabelImg is a graphical image annotation tool and label object bounding boxes in images

emezac/Lda2vec-Tensorflow

Tensorflow 1.5 implementation of Chris Moody's Lda2vec, adapted from @meereeum

emezac/libfaceid

A facial recognition library that seamlessly integrates multiple face detection and face recognition models. Also provides models for face pose estimation, age detection, gender detection and emotion detection.

emezac/MagicMirror

MagicMirror² is an open source modular smart mirror platform. With a growing list of installable modules, the MagicMirror² allows you to convert your hallway or bathroom mirror into your personal assistant.

emezac/MC-GAN

Multi-Content GAN for Few-Shot Font Style Transfer at CVPR 2018

emezac/MNIST_GAN

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

emezac/Neural_Differential_Equations

This is the code for "Neural DIfferential Equations" By Siraj Raval on Youtube

emezac/Notebooks

Machine learning notebooks in different subjects optimized to run in google collaboratory

emezac/pingpong

Test integration with angular

emezac/PresentaDonManolo

Jupyter notebook for showing Don Manolo

emezac/pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

emezac/PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

emezac/RamanujanMachine

emezac/shan-shui-inf

Procedurally generated Chinese landscape painting.

emezac/springBigCSV

emezac/style2paints

sketch + style = paints :art:

emezac/Temperature

Proof of concept to implement the Observer pattern with Ruby, is a nice way to implement a GUI-GTK app.

emezac/TensorFlow-Tutorials

TensorFlow Tutorials with YouTube Videos

emezac/tsfresh

Automatic extraction of relevant features from time series:

emezac/WaveFunctionCollapse

Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.