reading-go - Go 每日阅读和 Go 夜读 > Daily Reading Go and Night Reading Go - Go source reading and offline technical or another articles or discussion on every night

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Go 每日阅读和 Go 夜读 > Daily Reading Go and Night Reading Go - Go source reading and offline technical or another articles or discussion on every night.



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gopher-reading-list - A curated selection of blog posts on Go


Here is a reading list of blog posts about Go. It aspires to include only the most useful and relevant material that anyone writing Go should eventually read. By definition, the list is a work in progress. Rather than being comprehensive, the list is a curated selection fixed at 200 entries.

Reinforcement-Learning-Notebooks - A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python

  •    Jupyter

I wrote these notebooks in March 2017 while I took the COMP 767: Reinforcement Learning [5] class by Prof. Doina Precup at McGill, Montréal. I highly recommend you to go through the class notes and references of all the papers the intructors have posted on the website. These notebooks should be used while you read the book and go beyond the same with the referenced papers. I would suggest watching David Silver's videos and reading the book simultaneously. And when you are done with a few chapters, start implementing them. The algorithms follow a pattern and mostly are variants of each other. I have tried my best to explain each notebook's results and possible future directions.

criticalML - Toward ethical, transparent and fair AI/ML: a critical reading list for engineers, designers, and policy makers


In the past 5 years there’s been a lot of enthusiasm about AI and specifically machine learning and deep learning. As we continuously deploy AI models in the wild we are forced to re-examine what are the effects of knowledge symbolisation, generalisation and classification on the historical, political and social conditions of human life. We also need to remind ourselves that algorithms don’t exercise their power over us. People do. These sections aren't in any particular order. There's overlap and interaction between these topics that you can jump around as much as you want; Reading "out of order" could lead to interesting connections.

awesome-deep-learning-papers - The most cited deep learning papers

  •    TeX

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains. Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Also, after this list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap, has been created and loved by many deep learning researchers.

dissecting-reinforcement-learning - Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

  •    Python

This repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.

Deep-Learning-Papers-Reading-Roadmap - Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

  •    Python

You will find many papers that are quite new but really worth reading. I would continue adding papers to this roadmap.

xlsx - Google Go (golang) library for reading and writing XLSX files

  •    Go

xlsx is a library to simplify reading and writing the XML format used by recent version of Microsoft Excel in Go programs.The full API docs can be viewed using go’s built in documentation tool, or online at

DeepRL-Agents - A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

  •    Jupyter

This repository contains a collection of reinforcement learning algorithms written in Tensorflow. The ipython notebook here were written to go along with a still-underway tutorial series I have been publishing on Medium. If you are new to reinforcement learning, I recommend reading the accompanying post for each algorithm.

bitcoin-reading-list - a reading list for learning to program Bitcoin transactions


a reading list for learning to program Bitcoin transactions

t81_558_deep_learning - Washington University (in St

  •    Jupyter

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

DrQA - A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

  •    Python

A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA). Reading comprehension is a task to produce an answer when given a question and one or more pieces of evidence (usually natural language paragraphs). Compared to question answering over knowledge bases, reading comprehension models are more flexible and have revealed a great potential for zero-shot learning.

lip-reading-deeplearning - :unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures

  •    Python

The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information.

rwtxt - A cms for absolute minimalists.

  •    Go

A cms for absolute minimalists. Try it at rwtxt is an open-source website where you can store any text online for easy sharing and quick recall. In more specific terms, it is a light-weight and fast content management system (CMS) where you write in Markdown with emphasis on reading.

  •    CSS

This github blog theme is forked from zJiaJun. I use this repo to organise interesting papers, projects, websites, blogs and my reading/study notes.

jason - Easy-to-use JSON Library for Go

  •    Go

Jason is an easy-to-use JSON library for Go. Jason is designed to be convenient for reading arbitrary JSON while still honoring the strictness of the language. Inspired by other libraries and improved to work well for common use cases. It currently focuses on reading JSON data rather than creating it. API Documentation can be found on

xz - Pure golang package for reading and writing xz-compressed files

  •    Go

This Go language package supports the reading and writing of xz compressed streams. It includes also a gxz command for compressing and decompressing data. The package is completely written in Go and doesn't have any dependency on any C code. The following example program shows how to use the API.

awesome-appsec - A curated list of resources for learning about application security

  •    PHP

A curated list of resources for learning about application security. Contains books, websites, blog posts, and self-assessment quizzes. Maintained by Paragon Initiative Enterprises with contributions from the application security and developer communities. We also have other community projects which might be useful for tomorrow's application security experts.

gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.

  •    Go

Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer


Inspired 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.