planaria158 Goto Github PK

apache-shiro-tutorial-webapp

A step-by-step tutorial showing how to secure a web app with Apache Shiro

autoencoder

barlowtwins

My experiments with Barlow Twins

bert

mini-BERT for protein sequence analysis

bert-antibody-antigen-binding-energy

A study to calculate antibody-antigen binding using a custom BERT model

cs228-material

Teaching materials for the probabilistic graphical models class at Stanford

cs231n.github.io

Public facing notes page

deep-visualization-toolbox

DeepVis Toolbox

deepdream

diffusion

DDPM Diffusion model

gadsdc1

class repo

jmeter-ec2

Automates running Apache JMeter on Amazon EC2

mockserver

MockServer is a mock HTTP server that enables easy mocking of any system you integrate with via HTTP or HTTPS (i.e. services, web sites, etc) with clients written in Java, JavaScript and Ruby. MockServer also includes a proxy that introspects all proxied trafffic including encrypted SSL traffic and supports Port Forwarding, Web Proxying (i.e. HTTP proxy), HTTPS Tunneling Proxying (using HTTP CONNECT) and SOCKS Proxying (i.e. dynamic port forwarding).

mpi4py-examples

mpi4py examples

neural-networks-and-deep-learning

Code samples for my book "Neural Networks and Deep Learning"

nolearn

Abstractions around neural net libraries, most notably Lasagne.

pytorch-lars

"Layer-wise Adaptive Rate Scaling" in PyTorch

rnn-tutorial-gru-lstm

Language Model GRU with Python and Theano

rnn-tutorial-rnnlm

Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

spearmint

Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012

tensorflow

Computation using data flow graphs for scalable machine learning

vit

Vision Transformer

word2vec

This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.