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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

https://github.com/imhuay/Algorithm_Interview_Notes-ChineseTags | interview machine-learning deep-learning algorithm chinese leetcode |

Implementation | Python |

License | Public |

Platform | Windows Linux |

Your personal guide to Software Engineering technical interviews. Maintainer - Kevin Naughton Jr.

interview interview-questions interview-practice interview-preparation interview-prep algorithm algorithm-challenges algorithms algorithm-competitions technical-coding-interview leetcode leetcode-solutions leetcode-java coding-interviews coding-interview coding-challenge coding-challenges leetcode-questions interviewsThis repository contains solutions and resources for LeetCode algorithm problems. An excel table for quick review before interview is also provided in resources directory.

leetcode review interview coding-interviews interview-questions interview-practice interview-preparation algorithm algorithmsNote: Some of the code here is old and was written when I was learning C++. It might be possible that code is not safe or making wrong assumptions. Please use with caution. Pull requests are always welcome. Include contains single header implementation of data structures and some algorithms.

algorithm cpp interview-questions interview-practice data-structures datastructures c-plus-plus bit-manipulation tree leetcode-solutions leetcode string-manipulation:books: Computer Science Learning Notes

algorithm leetcode coding-interviewIn these tutorials, we will demonstrate and visualize algorithms like Genetic Algorithm, Evolution Strategy, NEAT etc. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

evolutionary-algorithm genetic-algorithm neuroevolution microbial-genetic-algorithm travel-sale-problem evolution-strategy es reinforcement-learning neural-network microbial-ga neat neural-nets travel-sales-problem nes evolution-strategies openai distributed-es machine-learning tutorialLeetCode，《剑指offer》中的算法题的题目和解法以及常见算法的实现

algorithm interview interview-questions leetcode solutionCodes of my MOOC Course <Play with Algorithm Interviews>. Updated contents and practices are also included. 我在慕课网上的课程《玩儿转算法面试》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。

algorithms interview-questions interview-practice leetcode imooc moocThis is another (work in progress) Chinese translation of Michael Nielsen's Neural Networks and Deep Learning, originally my learning notes of this free online book. It's written in LaTeX for better look and cross-referencing of math equations and plots. And I borrowed some finished work from https://github.com/tigerneil/neural-networks-and-deep-learning-zh-cn. To compile the source code to a PDF file, please make sure you have a latest TeX system installed. You can download and install a TeX distribution for your platform from http://tug.org.

This work is some notes of learning and practicing data structures and algorithm. This project is hosted on https://github.com/billryan/algorithm-exercise and rendered by Gitbook. You can star the repository on the GitHub to keep track of updates. Another choice is to subscribe channel #github_commit via Slack https://ds-algo.slack.com/messages/github_commit/. RSS feed is under development.

algorithm gitbook leetcodeInspired by Google Interview University. This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.

machine-learning deep-learning artificial-intelligence software-engineer machine-learning-algorithmsAn updated and curated list of readings to illustrate best practices and patterns in building scalable, available, stable, performant, and intelligent large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to billions of users. Understand your problems: scalability problem (fast for a single user but slow under heavy load) or performance problem (slow for a single user) by reviewing some design principles and checking how scalability and performance problems are solved at tech companies. The section of intelligence are created for those who work with data and machine learning at big (data) and deep (learning) scale.

system-design backend scalability site-reliability-engineering sre interview architecture devops site-reliability design-patterns back-end back-end-development interview-questions design-systems awesome-list microservices distributed-systems design-system tech big-dataPython implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

machine-learning deep-learning deep-reinforcement-learning machine-learning-from-scratch data-science data-mining genetic-algorithmThis content is part of a series following the chapter 2 on linear algebra from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts. I'd like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In my opinion, it is one of the bedrock of machine learning, deep learning and data science.

If you are a newbie of Git, please check this tutorial we have made. Please note, this repository is inspired from KrisYu, and here is the approve letter. However, it has been modified, added and improved to reflect our knowledge, wisdom and efforts.

leetcode algorithm leetcode-solutions data-structure interview coding hacker geeker offerThis project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.

deep-learning deep-reinforcement-learning gameIn these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. If you speak Chinese, visit 莫烦 Python or my Youtube channel for more.

reinforcement-learning tutorial q-learning sarsa sarsa-lambda deep-q-network a3c ddpg policy-gradient dqn double-dqn prioritized-replay dueling-dqn deep-deterministic-policy-gradient asynchronous-advantage-actor-critic actor-critic tensorflow-tutorials proximal-policy-optimization ppo machine-learningClick "Watch" to get an email notification once a month for Top 10 Machine Learning articles. Update will be made on major releases. Mybridge AI ranks articles by the number of shares, minutes read, and by its own machine learning algorithm.

machine-learning machine-learning-algorithms nlp neural-networks deep-learning reinforcement-learningMy solutions to some of the algorithm and data structure questions in Java

algorithm interview datastructures data-structures hackerrank hackerrank-solutions geeksforgeeks coding-interviews interview-practice interview-preparation java-8 cracking-the-coding-interview cracking-the-technical-interview cracking-code-interview coding-interviewkeras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

keras tensorflow theano reinforcement-learning neural-networks machine-learning
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