465 Open source projects that are alternatives of or similar to Cs224n Winter Together

Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿‍🔬

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pytorch 包教不包会

A minimalist deep learning library in Javascript using WebGL + asm.js. Run convolutional neural network in your browser.

A neural network for end-to-end music source separation

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Code for the Lovász-Softmax loss (CVPR 2018)

Neural Networks that invent their own encryption 🔑

Implementation of handwriting generation with use of recurrent neural networks in tensorflow. Based on Alex Graves paper (https://arxiv.org/abs/1308.0850).

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Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

Implementation of 1D, 2D, and 3D FFT convolutions in PyTorch. Much faster than direct convolutions for large kernel sizes.

A repository of state-of-the-art deep learning methods in computer vision

An open-source platform for automating tasks using machine learning models

A curated list of awesome Distributed Deep Learning resources.

Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

Self driving cars using NEAT

The course notes for Stanford's CS231A course on computer vision

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood

List of articles related to deep learning applied to music

A 2D Unity simulation in which cars learn to navigate themselves through different courses. The cars are steered by a feedforward neural network. The weights of the network are trained using a modified genetic algorithm.

Probabilistic reasoning and statistical analysis in TensorFlow

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Spying using Smartwatch and Deep Learning

simple neural network library in ANSI C

CS 253 Web Security course at Stanford University

Distributed Learning by Pair-Wise Averaging

Concise deep learning for JAX

Highway networks implemented in PyTorch.

RLgraph: Modular computation graphs for deep reinforcement learning

Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge

LATEX: TikZ package for drawing neural networks. Also available on CTAN at http://www.ctan.org/tex-archive/graphics/pgf/contrib/neuralnetwork

TensorHub is a library built on top of TensorFlow 2.0 to provide simple, modular and repeatable abstractions to accelerate deep learning research.

These are the lectures, slides, reading assignments, and problem sets for the Developing Apps for iOS 11 with Swift 4 CS193p course offered at the Stanford School of Engineering and available on iTunes U.

Get started with Machine Learning in TensorFlow with a selection of good reads and implemented examples!

Awesome machine learning model compression research papers, tools, and learning material.

[Tensorflow.js] AdVis: Exploring real-time Adversarial Attacks in the browser with Fast Gradient Sign Method.

Tensorflow implementation of CycleGAN

This toolbox is support material for the book on CNN (http://www.convolution.network).

💥 Fast matrix-multiplication as a self-contained Python library – no system dependencies!

Computer-Assisted Reporting and Data Journalism Syllabuses, compiled by Dan Nguyen

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GPU accelerated Deep Belief Network

Interactive Notation for Computational Graphs

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

Contains my explorations of TensorFlow 2.x

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Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020).

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

SAFE: Self-Attentive Function Embeddings for binary similarity