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louwill's Projects

algorithm-exercise icon algorithm-exercise

Data Structure and Algorithm notes. 数据结构与算法/leetcode/lintcode题解/

algorithm_interview_notes-chinese icon algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

awesome icon awesome

😎 Awesome lists about all kinds of interesting topics

awesome-face_recognition icon awesome-face_recognition

papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;

awesome-programming-books icon awesome-programming-books

经典编程书籍大全，涵盖：计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

awesome-python icon awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-shell icon awesome-shell

A curated list of awesome command-line frameworks, toolkits, guides and gizmos. Inspired by awesome-php.

chatterbot icon chatterbot

ChatterBot is a machine learning, conversational dialog engine for creating chat bots

chexnet-keras icon chexnet-keras

This project is a tool to build CheXNet-like models, written in Keras.

conv_arithmetic icon conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

cs224d-1 icon cs224d-1

Deep Learning for Natural Language Processing in Stanford

data-science-ipython-notebooks icon data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

dcgan-tensorflow icon dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

deep-learning-in-production icon deep-learning-in-production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

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