Knowledge Repository

knowledge repository with learning resources, examples, links for various data science / computer science topics

Contents

• Knowledge Repository
  • Contents
  • Other Lists & Study Guides
  • Data Science
    • Reading
    • Machine Learning Theory
    • Python for Data Science (by Framework)
    • Python for Data Science (by Topic)
    • Data Science Deployment / Production
    • R
  • Software Development
    • In General
    • Python (non-datascience)
    • Rust
    • Others
  • Systems Design
    • Big Data
    • Backend / Deployment / Distributed Systems
  • Web Development
  • Reading List
  • Read Archive

Other Lists & Study Guides

• 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

Data Science

Reading

• Articles
  • You might want to hire a Data Engineer instead of a Data Scientist
  • 7 Steps to mastering SQL for Data Science
  • AI and Deep Learning: 2017 in Review
  • Datacamp
  • How to get into the Top15 of Kaggle with Python
• Blogs/Webpages
  • Sebastian Raschka
  • Karpathy Blog
  • Francois Chollet (keras) Blog
  • Dataquest
  • wildml.com
• Books
  • Everyday Data Science
• Papers
  • Papers With Code - Browse State-of-the-Art Machine Learning Papers

Machine Learning Theory

• 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
  • Data Science Interview Questions
  • 160 Data Science Interview Questions
• Logistic Regression
  • Data Science Simplified Part 11: Logistic Regression
  • Instrumental Variables
• 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
    • deeplearning4j Introduction
• Unsupervised Learning
  • Categorical and numeric data
• NLP
  • How to Solve 90% of NLP Problems: A Step by Step Guide
  • EFF: AI Progress Metrics
• Meta-Learning
  • Unsupervised Meta-Learning for Few Shot Classification
• Imbalanced Classes
  • Dealing with Imbalanced Data: Undersampling, Oversampling and Proper CV

Python for Data Science (by Framework)

• Basics, Pandas, scikit-learn
  • Python Data Science Handbook
  • Missing Data/Imputation in pandas
  • Cross-Validation (sklearn)
  • auto-sklearn
  • out-of-memory dataframes
• Boosting / Bagging / Trees
  • xgboost
    • Introduction to Boosted Trees
    • Bagging using 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
    • Train-Test Split with ImageDataGenerator
  • 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
      • Tensorboard Distributions and histograms (stackoverflow)
      • How to show all my images in tensorboard
  • Inference
    • Improve CPU Inference Performance with intel MKL Libraries
  • TF 1.X -> 2.X
    • TF 2.0 AutoGraph & Eager mode explained
    • Effective TensorFlow 2
  • 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
      • TF-Coder Inductive Programming Library approach to generate (simple) TF-Expressions from Input & Output Data pairs
      • Einops DSL with readable, high-level abstractions for tensor operations. Supports numpy, pytorch, tensorflow, and others.
  • Object Detection
    • Hand Detector (Egohands Dataset) - Code
  • NLP
    • CNN for Text Classification
    • word2vec (TensorFlow Tutorial)
    • CNN for Text Classifcation (Kim 2014)
• Production / Deployment
  • TFRT A performant and modular runtime for TensorFlow
  • tensorflowjs
    • Deep Learning in your Browser - a brisk guide
  • 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
    • Deep Learning with Python (Francois Chollet) - Code
    • Examples
  • Integration
    • With Sciikit-learn API
  • Advanced
    • Combining Different Filter Lengths in 1D-Convolutional Layers
    • Dynamic k-max Pooling
    • How to Implement non-static CNN - Kim (2014), 2
    • Extract Feature Weights
    • 2D vs 1D Convolutions for Text
    • Incorporating Word Vectors rather than using an Embedding class
    • MNIST Transfer Learning
• 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

Python for Data Science (by Topic)

• Anomaly Detection
  • Anomaly Detection, Recommender Systems and Scaling
  • Online anomaly detection
  • Cross validation for anomaly detection using autoencoder
  • Machine Learning Class Notes Lecture 16: Anomaly Detection¶
  • arXiv: Model Selection for 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
      • ROS toolbox face action recognition
      • OpenFace - face action detection
      • deep region and multi-label learning
      • Multi-View Dynamic Facial Action Unit Detection
      • Deep Region and Multi-Label Learning for Facial Action Unit Detection
    • 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
    • Multi-Label Classification using SmallerVGGNet and Keras
  • 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
    • OCR using tesseract and opencv
• 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
• Speech Recognition
  • Speech Recognition in Python
• Time Series
  • Open ML Course 9: Time Series Analysis in Python
  • A Guide to Time Series Forecasting with ARIMA in Python
  • SARIMAX
    • Sarimax not converging
• Recommender Systems
  • Best practices
• Reinforcement Learning / Games
  • Environments
    • AirSim Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
    • RLTrader A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym
    • SafetyGym OpenAPI Gym + Safety constraints
• Novelty Algorithm
  • AutoML Zero use Evolutionary Search to discover ML algorithms from scratch using only basic math operations

Data Science Deployment / Production

• 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…

R

• Introduction to Statistical Learning with Applications in R

Software Development

In General

• Learnanything
• learnXinYminutes
• devhints
• Learn by example: Command Line Text Processing
• Components
  • What is a Lambda Function?
  • An Intro to Compilers
• 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
      • visualgo
      • Algorithm visualizer
      • Data Structure Visualizations
    • Books
      • Algorithms in C 5 part book
      • Data Structures and Abstractions with Java (4th edition) book
      • Problem solving with algorithms and adata structures using python (2nd ed.)
    • Competitive Programming
      • leet Code
      • hackerrank
    • 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."
  • 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
  • How to do a code review
• Git
  • Interactive Tutorial
• CI/CD
  • I am a mediocre Developer
• 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

Python (non-datascience)

• Courses / Books
  • The Hitchhiker's Guide to Python - opinionated guide
  • Automate the Boring Stuff with Python
  • Full Stack Python
  • Fluent Python
  • Effective Python
  • Python Programming and Numerical Methods: A Guide for Engineers and Scientists
  • Advanced Python Courses
• 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
  • All algorithms implemented in Python
• 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
  • Bulding Rich Terminal Dashboards
• 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
• Telethon (Telegram API Wrapper)
  • Telethon API Documentation
  • Telegram API Introduction
  • How to Create and Deploy a Telegram Bot
• Webscraping
  • Web Scraping 101 with Python

Rust

• 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
  • Choosing a Rust Web Framework: 2020 Edition
• Blog
  • Scala Developer Journey Into Rust

Others

• C/C++
  • Learning the Language
    • The C Programming Language
    • Learn C the Hard Way
    • Modern C
    • Learn C in Y minutes
    • Learn C++ in Y minutes
    • Learn C++ by Building a crossword puzzle youtube tutorial series
  • Algorithms & Data Structures
    • Algorithms implemented in C++
  • 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
  • Learn You a Haskell for Great Good
  • Write Yourself a Scheme in 48 Hours
  • Graham Hutton Haskell YT Lecture - course link
• Java / Scala
  • Awesome Java
  • Helsinki MOOC
  • JSON / Swagger
    • Jackson
      • Swagger and polymorphic type handling with Jackson
      • Looking for a good example of polymoprrhic serialization/deserialization using jackson
• 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
    • All algorithms implemented in js
  • d3.js
    • Hitchhiker's Guide to d3.js

Systems Design

Big Data

• Hadoop
  • Hadoop - The Definitive Guide (O'Reilly)
  • Hadoop starter kit - Free Hadoop Cluster (3 nodes)

Backend / Deployment / Distributed Systems

• 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
  • Dockerfile Linting
  • Clean build-time secrets
  • Using multi-stage builds
• 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
    • Apache Airflow with Kubernetes Executor and MiniKube
    • KEDA
      • KEDA jobs with Azure Store Queues
    • Celery
      • Celery + Kubernetes
• nginx
  • nginx config generator ui
• Caching / In-Memory DB
  • KeyDB - Multithreaded Redis fork ("5 times faster")
• Loggin & Monitoring
  • Prometheus Basics
• RESTful APIs
  • Tooling
    • postwoman postman alternative
• Video Streaming
  • Building a media streaming server using go and hls protocol

Web Development

• Chrome Dev Tools: Overview
• MDN
• QuickDBD
• JSFiddle
• What the Heck is the Event Loop Anyway?
• Browser Support
  • caniuse.com
• CSS
  • BEM - Block Element Modifier Convention
  • SMACSS - Scalable and Modular Architecture for CSS Convention
  • css-grid - css grid specification
• UI
  • Adobe Color
• UX
  • What is User Experience Design? - Overview, Tools, and Resources
  • Usability 101
  • Testing
    • No-Code Regression Tests
• Frameworks
  • Bootstrap
  • Foundation
• Get Inspired
  • Dribbble
  • Behance
  • awesome-inspire

Reading List

• 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
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