knowledge repository with learning resources, examples, links for various data science / computer science topics
- Knowledge Repository
- Google Interview University - Multi-month study plan to becoming a Google software engineer
- Dataquest: How to Setup a Data Science Blog
- Fullstack Web-Developer Path
- Frontend Developer Beginner Resources
- Front-End Handbook 2017
- Path to a free self-taught education in Data Science!
- Date Engineer Roadmap 2020
- DeepMind Curated Resource List
- Machine Learning Roadmap 2020
- Machine Learning Mindmap
- Machine Learning Roadmap
- HN Academy online courses recommended by hacker news community
- Articles
- Blogs/Webpages
- Books
- Papers
- Papers With Code - Browse State-of-the-Art Machine Learning Papers
- General
- An Overiew of Gradient Descent
- Machine Learning Glossary
- Most Cited Deep Learning Papers
- Precision and Recall
- Pattern Recognition and Machine Learning (Code examples) Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"
- Google AI Eduction "learn from ML experts at Google"
- MOOC
- fast.ai - Practical Deep Learning for Coders
- Google ML Crash Crouse - very crash course
- Hardvard CS109: Data Science
- Stanford CS221: AI
- Stanford CS224d: Deep Learning for NLP
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition
- Machine Learning Mastery
- Deep Learning with Google (Udacity)
- Dive into Deep Learning (berkeley) Theory & Practice
- mlcourse.ai Open Machine Learning Course (unsupervised learning, tress, feature engineering, boosting etc. - no deep learning)
- Coursera Deep Learning Specialization deeplearning.ai, Andrew Ng
- Coursera TensorFlow in Practice
- Full Stack Deep Learning bridge the gap from training machine learning models to deploying AI systems in the real world.
- MIT: Computational Thinking Math / CV / Julia class with 3blue1brown
- Prepping the Interview
- Logistic Regression
- Decision Trees
- [Random Forest for complete beginners](https://victorzhou.com/blog/intro-to-random-forests/
- Neural Networks / Deep Learning
- A Recipe for Training Neural Networks Andrej Karpathy
- deeplearningbook THE book by Bengio/GoodFellow/Courville. From NN to autoencoders..
- Deep Learning in Neural Networks: An Overview (paywall)
- Dive into Deep Learning
- CNN
- "The Best explanation of NN on the internet"
- Beginner's Guide to Understanding CNN
- 100 Best CNN Videos
- CNN Explainer An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs)
- Generative Adversarial Networks
- Unsupervised Learning
- NLP
- Meta-Learning
- Imbalanced Classes
- Basics, Pandas, scikit-learn
- Boosting / Bagging / Trees
- xgboost
- catboost A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU
- lightgbm A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
- TensorFlow & new Keras (since TF 2.0)
- Data Ingest
- Learn
- TensorFlow-Book
- TensorFlow for Machine Intelligence
- TensorFlow Machine Learning Curriculum (official)
- eat TensorFlow 2 in 30 days easily digestible tf 2 book & study plan, includes a lot of the in-depth mechanics
- TensorFlow Developer Certificate
- Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide This article contains details of how the LSTM model was actually trained on Python using TensorFlow 2 with Keras API.
- TensorFlow Roadmap 📡 Organized & Useful Resources about Deep Learning with TensorFlow
- Inside TensorFlow
- Training
- Learning Rate Schedules & Decay
- How do I find variable names and values in a checkpoint
- Hyperparameter Optimization
- keras-tuner: hyperparameter optimization
- hyperas wrapper around hyperopt for faster prototyping with keras models
- autokeras AutoML system based on Keras
- Tensorboard
- Inference
- TF 1.X -> 2.X
- Frameworks & Integration
- ludwig Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.
- TensorOps Arithmetic Helpers / DSL
- Object Detection
- NLP
- Production / Deployment
- TFRT A performant and modular runtime for TensorFlow
- tensorflowjs
- TensorFlow Lite
- Interpret & Visualize Models
- Interpreting Tensorflow models with tf-explain
- gradio: TensorFlow UI Components
- Tensporspace neural network 3D visualization framework built with tensorflowjs
- Keras (pre TensorFlow)
- Learn
- Integration
- Advanced
- pytorch
- torch2rt An easy to use PyTorch to TensorRT converter
- TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision
- botorch bayesian optimization in pytorch
- Anomaly Detection
- Computer Vision
- What is optical flow and why does it matter in deep learning
- Action Recognition
- awesome action recognition
- Real-time Action detection demo for the work Actor Conditioned Attention Maps
- 2-stream CNN architectures for action detection and recognition with attention filtering
- action recognition github search
- Real-Time Action Detection
- temporal action detection with SSN
- Weakly supervised action recognition and detection
- human action recognition with keras
- cascaded boundary regression for action detection
- action detection dRNN
- Single-Stream Temporal Action Detection in Untrimmed Videos
- Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
- Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
- hand-detection motion control
- Cross Dataset framework that both detects actions in a crowded scenes
- Graph Distillation for Action Detection
- Kinect Real-time (Online) Action Detection
- Face
- Framework
- Videoflow Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
- Classification
- Face Recognition
- face_recognition The world's simplest facial recognition api for Python and the command line
- DeepFaceLab DeepFaceLab is a tool that utilizes machine learning to replace faces in videos. Includes prebuilt ready to work standalone Windows 7,8,10 binary (look readme.md).
- faceswap Deepfakes Software For All
- Object Detection & Image Segmentation
- Detectron2 Framework Framework; pytorch-based (from facebook)
- Object Removal Just draw a bounding box and you can remove the object you want to remove
- OCR
- Feature Engineering
- Featuretools - automatic feature engineering
- NLP
- Models
- Transformers State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
- Topic Modeling with Gensim
- Text Summarization - sumeval
- State of the art text classification with universal language models
- Models
- Speech Recognition
- Time Series
- Recommender Systems
- Reinforcement Learning / Games
- Novelty Algorithm
- AutoML Zero use Evolutionary Search to discover ML algorithms from scratch using only basic math operations
- Distributed Learning
- Ray A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning libr…
- Learnanything
- learnXinYminutes
- devhints
- Learn by example: Command Line Text Processing
- Components
- Knowledge
- Teach Yourself Computer Science
- Things every programmer should know
- Naming Conventions
- Functional Programming Jargon
- Computer Science for Engineers
- Books
- Must Read books for Developers without CS Degree (HN)
- The Art of Computer Programming "Few programmers have read it from cover to cover, but it remains perhaps the ultimate and authoritative in-depth reference on the subject."
- Big-O Cheat Sheet
- Algorithms and Data Structures, Coding Interviews
- Study Gudies
- geeksforgeeks theory, examples, practices, study guides
- Coding Interview University multi-month study plan for going from web developer (self-taught, no CS degree) to software engineer for a large company.
- All algorithms implemented in Python
- All algorithms implemented in js
- Visualizations
- Books
- Competitive Programming
- Advanced
- Introduction to Algorithms (3rd ed.) "The section on algorithmic reasoning is awesome, and it also offers more details on complexity analysis and mathematical tools."
- Study Gudies
- Software Architecture
- The Architecture of Open Source Applications open source software architects talk about their projects
- Software Architect Roadmap
- Software Architecture Patterns - Free O'Reilly Book
- Design Patterns (wikipedia) The original OOP book
- The C4 Model for visualizing software architecture "Context, Containers, Components and Code"
- Become a better Software Architect
- Software Architecture Guide
- Techniques
- Git
- CI/CD
- CLI
- The Art of Command Line
- htop explained
- tldr simplified man pages for cli programs
- explainshell breaks down and tries to explain shell commands & pipes
- Bash
- bash guide learn bash
- bash hackers wiki
- Conditional constructs if statements
- mastering jq jq is a command line json parser
- Tooling
- Analysis-tools.dev Compare 483+ Analysis tools for all Languages
- Courses / Books
- Language Features, Paradigms, Abstractions, Advance Python
- Python 3 Patterns Recipes and Idioms
- Functional Programming in Python
- Composing Programs
- Object Oriented Programming
- What the fck PythoN! Exploring and understanding Python through surprising snippets.
- The Little book of Python Anti-Patterns
- pytudes Python programs, usually short, for perfecting particular programming skills. by Peter Norvig
- pysanity Opinionated Coding Guidelines/philosophy
- How to NEVER use lambdas An inneficient and yet educatonal [sic] guide to the proper misuse of the lambda construct in Python 3.x [DO NOT USE ANY OF THIS EVER]
- Basics
- Using Config files - YAML, JSON, etc.
- Number Formatting
- Example Google Style Python Docstrings
- Algorithms and Data Structures
- Tooling (Testing/Linting/Runtimes/Compiler)
- Numba: A High Performance Python (JIT) compiler
- Python resources for development
- Hypermodern Python Dev Environment
- Type Checking
- Our Journey to type checking.. - Dropbox Blog
- Python Type Hints
- Monkeytype generates type hints by collecting runtime types
- Async / Concurrency
- Async Python is not faster - HTTP server benchmarks
- Sync vs. Async Python: What is the Difference?
- Asynco IO in Python: A Complete Walkthrough
- Concurrency Python discussion (hackernews)
- Python concurrency - The tricky bits - An exploration of threads, processes, and coroutines in Python
- CLI
- Django
- GraphQL in Django
- How to manage concurrency in Django Models
- Concurrency control in Django model
- Django-celery (pypi) - Celery Integration for Django
- Django-rq - Django integration for RQ (Redis Queue)
- Databases
- Introduction to Databases (Stanford)
- Better PostgreSQL testing with Python: announcing pytest-pqsql and pqmock
- pytest-postgresql 2.4.0 (pypi)
- Database testing in python, postgresql (stackoverflow)
- Databases (Full Stack Python)
- Database DevOps
- Roundhouse Database migration tool using version control
- Flyway database version control
- Redgat Database DevOps Enterprise solution ($$)
- Alembic lightweight db migration tool for python sqlalchemy
- pyscopg postgresql driver for python
- Database DevOps
- Telethon (Telegram API Wrapper)
- Webscraping
- Language
- The Rust Programming Language Official Book
- Rust By Example Companion Examples to the Official Book
- The Rust Reference
- The Rustonomicon - The Dark Arts of Unsafe Rust
- easy rust
- Hands-On Concurrency with Rust: Confidently build memory-safe, parallel, and efficient software in Rust - Book
- Hands-On Data Structures and Algorithms with Rust - Book
- Programming Rust: Fast, Safe Systems Development - ⭐ Supposedly the best book for actual rust programming for experienced software developers
- Learning Rust with entirely too many linked lists rust intricacies and idiomatic rust
- rustlings Small exercises to get you used to reading and writing Rust code!
- tl;dr Rust quick high-level overview of patterns in Rust (with comparision to Go)
- OMG WTF RS helpful rust resources
- CS110L: Safety in Systems Programming Stanford undergrad Course
- Web-Frameworks
- Blog
- C/C++
- Learning the Language
- Algorithms & Data Structures
- Safety
- awesome-saffety-critical list of resources about programming practices for writing safety-critical software
- Testing
- GoogleTest unit test framework
- Tooling
- libc
- xeus cling jupyter c++ kernel
- Go
- The ecosystem of the Go programming language - Proper overview of all resources you would need to get into go
- Practical Go Lessons - In depth book about all things go & cs basics
- Learn Go by porting a medium-sized web backend from Python
- Learn Go with tests
- Go by eample
- Using Go modules
- 1000+ Hand-Crafted Go Examples, Exercises, and Quizzes
- How I write Go HTTP Services after Seven Years
- Haskell
- Java / Scala
- JavaScript
- State of JS collecting data from over 20,000 developers to identify current and upcoming trends.
- Learn
- ECMAScript 2020 Language Specification official docs for the newest js iteration
- Eloquent JavaScript highly recommended js book
- JavaScript for impatient programmers (ES2020 edition)
- you don't know js deep-dive into js core mechanisms
- Exploring JS couple js books which are highly recommended
- Build your own React Project to build a react-clone from scratch
- Mostly adequate guide to FP
- Algorithms and Data Structures
- d3.js
- Hadoop
- Hadoop - The Definitive Guide (O'Reilly)
- Hadoop starter kit - Free Hadoop Cluster (3 nodes)
- Resources
- awesome-scalability The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
- system design primer Learn how to design large-scale systems
- awesome system design another list
- Data Serialization
- Flexbuffers
- Hackernews Discussion
- Protobuf
- Apache Arrow Flight faster(?) gRPC alternative
- Docker
- Kubernetes
- Magic Sandbox Platform Kubernetes Bootcamp & Learning Platform
- Katacoda learn k8s interactively in the browser
- 10 most common mistakes using kubernetes
- Common mistakes using Kubernetes (hackernews)
- A gentle Introduction to Kubernetes
- Kubernetes YAML Generator
- Pulumi - Kubernetes YAML SDK / Bindings for ts, python, go, ...
- Validating Kubernetes YAML for best practice and policies
- Workflows / Pipelines / Streaming / Message Systems
- nginx
- Caching / In-Memory DB
- KeyDB - Multithreaded Redis fork ("5 times faster")
- Loggin & Monitoring
- RESTful APIs
- Tooling
- postwoman postman alternative
- Tooling
- Video Streaming
- Chrome Dev Tools: Overview
- MDN
- QuickDBD
- JSFiddle
- What the Heck is the Event Loop Anyway?
- Browser Support
- CSS
- UI
- UX
- What is User Experience Design? - Overview, Tools, and Resources
- Usability 101
- Testing
- Frameworks
- Get Inspired
- Improve Your Python: Understanding Unit Testing
- http://blog.adnansiddiqi.me/getting-started-with-apache-kafka-in-python/
- https://news.ycombinator.com/item?id=17286810
- 1st Place Solution Summary: CVSSP & Visual Atoms (0.627) | Kaggle
- Multi-label classification with Keras - PyImageSearch
- Real-time Human Pose Estimation in the Browser with TensorFlow.js
- https://grunfy.com/scaler.html
- python - Saving and loading objects and using pickle - Stack Overflow
- https://reddit.com/r/web_design/comments/8p9dmm/favorite_youtube_channels_where_you_can_watch/
- http://www.instructables.com/id/Arduino-Soil-Moisture-Sensor/
- https://www.remoteonly.org/
- https://nickjanetakis.com/blog/how-to-pick-a-good-monitor-for-software-development
- https://www.blog.google/topics/machine-learning/fighting-fire-machine-learning-two-students-use-tensorflow-predict-wildfires/
- RESTful APIs - An accurate description - Johno the Coder
- https://medium.com/@_aerdeljac/learn-how-to-create-a-simple-blog-with-react-node-c05fa6889de3
- The Programmer’s Guide to a Sane Workweek
- How to become a part-time programmer: an interview with an expert
- How I Start.
- Bear - Notes for iPhone, iPad and Mac
- https://nyti.ms/2GuY9MO
- https://news.ycombinator.com/item?id=17097188
- Ask HN: Anyone designs financial engineering tools as a hobby? | Hacker New
- https://learndigital.withgoogle.com/zukunftswerkstatt/certification?utm_source=Instagram&utm_medium=cpc&utm_content=ig_de_education&utm_campaign=ig_de_education&dclid=CNGC8YXQidsCFUga4AodiUAIHQ
- Tradeshift Text Classification | Kaggle
- https://m.youtube.com/watch?v=GyCv_S42Tak
- Creating your own estimator in scikit-learn
- Trend Analysis Reveals the Most Loved and Hated Cryptocurrencies - Bitcoin
- Learn to Build Web Applications with Flask and Docker
- Introduction to Decision Tree Learning – Heartbeat
- keras/mnist_transfer_cnn.py at master · keras-team/keras
- Not another MNIST tutorial with TensorFlow - O'Reilly Media
- Number plate recognition with Tensorflow - Matt's ramblings
- Methylcobalamin and Adenosylcobalamin – Vegan Health
- https://dev.to/vipinjain/5-keys-to-optimizing-your-code-review-process-341e
- https://dev.to/amangautam/softer-skills-that-make-you-a-better-programmer--2g3e
- https://dev.to/fabrik42/my-productivity-boosters--a-random-collection-of-tricks-and-tools--what-are-yours-28fm
- How can I install CUDA 9 on Ubuntu 17.10 - Ask Ubuntu
- Word2vec Tutorial | RARE Technologies
- A list of cool Chrome DevTools Tips and Tricks
- https://medium.com/@sadatnazrul/the-dos-and-donts-of-principal-component-analysis-7c2e9dc8cc48
- https://mobile.twitter.com/tmobileat/status/981785213549383680
- https://reddit.com/r/YouShouldKnow/comments/8a9egh/ysk_active_listening_a_technique_developed_by_the/
- World's Most Popular API Framework | Swagger
- machine learning - What is the difference between test set and validation s
- machine learning - How to use k-fold cross validation in a neural network -
- Install Python 3.6 Keras 2.1.5 Tensorflow GPU 1.6 on Windows 10 (3/12/2
- https://hn.premii.com/#/comments/16738817
- Wrap Your Repo with Anaconda Project – Daftcode Blog
- Managing Python Project with Conda – Little Big Programming – Medium
- My Python Environment Workflow with Conda | tdhopper.com
- Anaconda-Platform/anaconda-project: Tool for encapsulating, running, and re
- https://towardsdatascience.com/topic-modelling-in-python-with-nltk-and-gensim-4ef03213cd21
- https://reddit.com/r/web_design/comments/88v07j/any_good_blogs_to_read_up_on_trends_in_web_design/
- https://dev.to/rpalo/data-science-cardio-1---weather-20l7
- Jane Street Tech Blog - Putting the I back in IDE: Towards a Github Explore
- http://www.shortscience.org/paper?bibtexKey=journals/corr/1606.04474
- Data Augmentation Techniques in CNN using Tensorflow
- Google Cloud Platform Blog: Introducing Cloud Text-to-Speech powered by Dee
- HN Domain Leaderboard
- Background Gradient Colors | Eggradients.com
- A Practical Guide to SVGs on the web
- https://otter-in-a-suit.com/blog/?p=164
- https://medium.freecodecamp.org/learning-react-roadmap-from-scratch-to-advanced-bff7735531b6
- Five Key Git Concepts Explained the Hard Way – zwischenzugs
- https://www.learnopencv.com/author/spmallick/
- Inter UI font family
- https://thekevinscott.com/common-patterns-for-analyzing-data/
- https://hn.premii.com/#/article/16556732
- https://design.google/library/choosing-web-fonts-beginners-guide/
- Asana – Ihre Projektmanagement-Software zur Aufgabenverwaltung & zum Nachve
- Mathpix
- AI and Deep Learning in 2017 – A Year in Review – WildML
- Introduction to Learning to Trade with Reinforcement Learning – WildML
- Asana-Preise für Premium- und Enterprise-Pläne · Asana
- How we built Hamiltix.net for less than $1 a month on AWS | Bad Sector Labs
- Changes over time · Creative Coding
- http://hackingforartists.com/
- A Development Methodology for Deep Learning – Intuition Machine – Medium
- The Definitive JavaScript Handbook for your next developer interview
- https://github.com/Mrgemy95/Tensorflow-Project-Template?files=1
- Modern CSS Explained For Dinosaurs – Actualize – Medium
- Google Developers Blog: Introduction to TensorFlow Datasets and Estimators
- RPubs - Logistic Regression Coefficients Interpretation
- https://www.datascienceatthecommandline.com/
- A Beginner’s Guide to Data EngineeringA Beginner’s Guide to Data Engineering – Part I
- A minimalist guide to tmux | Hacker News
- A minimalist guide to tmux – Actualize – Medium
- Code Less, Think More… Incrementally! – gitconnected
- https://www.facebook.com/Engineering/posts/10156072334557200
- https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e
- best practice for configuring script (ie. parameter setting) : Python
- (1) What kind of A/B testing questions should I expect in a data scientist
- The Problem With Problems – Black n White – Medium
- Tutorial: How to label thousands of images using the crowd
- Education | Kaggle
- Jay Nagpaul - Getting Started With Algorithmic Crypto Trading
- CS9: Problem-Solving for the CS Technical Interview
- Ask HN: How do I prepare for an interview for AMZ/GOOG/APL/FB? | Hacker New
- Stack Overflow Documentation Data Dump : Stack Exchange, Inc. : Free Downlo
- GoalKicker.com – Free Programming Books
- Imposter syndrome
- Welcome to Java for Python Programmers — Java for Python Programmers
- Turning Design Mockups Into Code With Deep Learning - FloydHub Blog
- https://www.reddit.com/r/webdev/comments/7pfvum/2018s_web_developers_roadmap_this_thing_is/?st=JCA7DW0O&sh=4299652f
- http://www.bbc.co.uk/news/world-us-canada-42614777
- Corey Schafer - YouTube - YouTube
- https://www.amazon.com/Java-Programming-Language-4th/dp/0321349806
- Complete Guide to Topic Modeling - NLP-FOR-HACKERS
- Not another MNIST tutorial with TensorFlow - O'Reilly Media
- https://www.reddit.com/r/learnjavascript/comments/7o40gv/what_is_the_best_book_to_use_as_a_primary_source/?st=JC1OTWAX&sh=7dc2afe5
- CS50 at Harvard
- Oh, shit, git!
- Ten years of professional blogging – what I’ve learned | Hacker News
- Learn HTML/CSS and JavaScript By Creating A Basic Calculator. Great For Beg
- Ask HN: How can I become a self-taught software engineer? | Hacker News
- permalink
- faq - learnprogramming
- The Smart, the Stupid, and the Catastrophically Scary
- Model–view–controller - Wikipedia
- Text Classification with NLTK and Scikit-Learn · Libelli
- Fermat's Library | Bitcoin: A Peer-to-Peer Electronic Cash System annotated
- Enlight
- Turning Vim Into An R IDE – Kade Killary – Medium
- Automated code review for Python, JavaScript, and CSS. | Lintly
- Syncing a fork - User Documentation
- First Timers Only - Get involved in Open Source and commit code to your fir
- Structuring Your Project — The Hitchhiker's Guide to Python
- Improve Your Python: Python Classes and Object Oriented Programming
- python - OOP : Trying to design a good class structure - Stack Overflow
- A simple example of Python OOP development (with TDD) - Part 1 - The Digita
- Top 15 Python Libraries for Data Science in 2017 – ActiveWizards: machine l
- Learned to code, got interview at Google but I wish I was told...
- How to get crypto-currencies rates and more in Google Sheet
- visualization - Sankey Diagrams in R? - Stack Overflow
- Why CSS Grid is better than Bootstrap for creating layouts
- https://medium.com/windfalldata/the-data-science-stack-at-windfall-data-e1e6bc3c4c8f
- http://datawanderings.com/2017/12/01/geek-christmas-best-books-data-scientist/
- Jekyll Themes | Made Mistakes
- How I’m Using Jekyll in 2017 | Made Mistakes
- Today I Learned | Made Mistakes
- visual studio code - how to insert current date time in vscode? - Stack Ove
- Weekly Machine Learning Opensource Roundup – Nov. 30, 2017 | PocketCluster
- Random forest - Wikipedia
- AdaBoost - Wikipedia
- Introduction to Boosted Trees — xgboost 0.6 documentation
- tradeshift-text-classification/src at master · niderhoff/tradeshift-text-cl
- Mushroom Classification | Kaggle
- How to get started with data science in containers | No Free Hunch
- Algorithm Design: Parallel and Sequential
- (1) Minimalistic Social App in few days. | LinkedIn
- Machine Learning FAQ
- Introduction · IOTA Guide and FAQ
- Understanding Ethereum Smart Contracts - Gjermund Bjaanes
- How to Solve It - Wikipedia
- How to Solve it by Computer - Wikipedia
- Hey developers, do you use Stack overflow.? – codeburst
- Seven Clean Steps To Reshape Your Data With Pandas Or How I Use Python Wher
- The impossibility of intelligence explosion – François Chollet – Medium
- Population based training of neural networks | DeepMind
- permalink
- Home · Rdatatable/data.table Wiki
- Distill is dedicated to making machine learning clear and dynamic
- A Year in Computer Vision
- Ask HN: How to become the first result of a Google search for a name? | Hac
- Lending Club | Looker
- How To Make Windows Fonts Look Like Mac Fonts
- How to make your own trading bot – codeburst
- Analyzing tf-idf results in scikit-learn - datawerk
- More than a Million Pro-Repeal Net Neutrality Comments were Likely Faked
- Flask and MongoDB Project. A Simple Reddit Reader
- machine learning - What is the difference between test set and validation s
- Yet Another Lambda Tutorial | Python Conquers The Universe
- A Guide to Becoming a Full-Stack Developer in 2017 – Coderbyte – Medium
- Ask HN: Books on specific topics that have applied to many areas of your li
- Stop Using word2vec | Stitch Fix Technology – Multithreaded
- Jekyll CBCD Pipeline to the Cloud - Cloudy Minds
- Dreaming of names with RBMs
- [AI] The fastest way to identify keywords in news articles — TFIDF with Wik
- How to Build a Great Tech Company That VCs Will Love
- 19 Best fonts to use in a terminal emulator as of 2017 - Slant
- 101 Best programming fonts as of 2017 - Slant
- Machine Learning Top 10 Articles for the Past Month (v.Nov 2017)
- I interviewed at five top companies in Silicon Valley in five days, and luc
- Software 2.0 – Andrej Karpathy – Medium
- Feature Visualization
- Understanding Hinton’s Capsule Networks. Part I: Intuition.
- http://www.youtube.com/playlist?list=PLME-KWdxI8dcaHSzzRsNuOLXtM2Ep_C7a
- https://www.reddit.com/r/programming/comments/7e59fl/how_do_computers_read_code_a_great_video/?st=JA8P32CX&sh=8112f831
- The Incredible Ways Heineken Uses Big Data, The Internet of Things And Arti
- Nipun Ramakrishnan's answer to Should I memorize the maths and algorithms w
- www.deeplearningbook.org/contents/rnn.html
- 1503.02531.pdf
- https://www.reddit.com/r/MachineLearning/comments/7dzh87/d_how_to_build_a_portfolio_as_a_machine/?st=JA79X4JT&sh=1c2080d0
- python - How to write a custom estimator in sklearn and use cross-validatio
- A Cookbook for Machine Learning: Vol 1
- Dark Mode List
- An On-device Deep Neural Network for Face Detection - Apple
- Pachyderm - Scalable, Reproducible Data Science
- How to Learn Pandas – Towards Data Science
- Essential Algorithms Every ML Engineer Needs to Know
- Regularization in Machine Learning – Towards Data Science
- Startup Mistakes: Choice of Datastore - Stavros' Stuff
- Exploring AirBnB’s Knowledge Repo: A Curated Knowledge Sharing Platform
- A Guide to Natural Language Processing - Federico Tomassetti - Software Arc
- Directory Contents
- The Future of Kaggle & Data Science: Quora Session Highlights with Anthony
- How to Learn Pandas – Theodore Petrou – Medium
- CodeTriage
- Machine Learning Algorithms: Which One to Choose for Your Problem
- Run-time method patching in Python - Tryolabs Blog
- Use Keras Deep Learning Models with Scikit-Learn in Python - Machine Learni
- Scaling Knowledge at Airbnb – Airbnb Engineering & Data Science – Medium
- How startups such as Dropbox, Airbnb, Groupon and others acquired their fir
- Docker for data science, building a simple jupyter container
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