Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM

『밑바닥부터 시작하는 딥러닝』(한빛미디어, 2017)

Protocol Buffers - Google's data interchange format

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

An interactive deep learning book with code, math, and discussions, based on the NumPy interface.

Reference models and tools for Cloud TPUs.

Models built with TensorFlow

Intermediate Computational Graph Representation for Deep Learning Systems

Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)

This research aims at simply deploying CNN(Convolutional Neural Network) on mobile devices, with low complexity and high speed.

Acceleration package for neural networks on multi-core CPUs

RTL, Cmodel, and testbench for NVDLA

Bare bones Python implementations of Machine Learning models and algorithms. Aims to cover everything from Data Mining techniques to Deep Learning.

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

Mastering Bitcoin - Unlocking digital currencies - Early Release Draft

OpenBLAS is an optimized BLAS library based on GotoBLAS2 1.13 BSD version.

Development kit for MIT Scene Parsing Benchmark

Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

A hardware-accelerated machine intelligence library for the web.

Auto-WEKA

Collection of generative models in Tensorflow

Summaries and notes on Deep Learning research papers

Re-implementation of TensorFlow in pure python, with an emphasis on code understandability

Pruning and other network surgery for trained Keras models.

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

An experimental technique for efficiently exploring neural architectures.

[ko] 패스트캠퍼스 강의 자료