2019. January Reading List
- Jetbrains Kotlin Workshop
- Kubernetes The Hard Way
- Kubernetes Fundamentals
- Event Sourcing Microservices Example with Spring, Kubernetes, and Docker
- RxJava – 5 Steps to Reactive Enlightenment
- Creating Kotlin DSLs
- Purrier Series (Meow) and Making Images Speak
- Performance Under Load
- Automating FTP deployment with Travis CI and Gulp
- Contextual Logging with Reactor Context and MDC
TIL
Soft
Books
- Deep Learning
- [ ]
Videos
- Rediscovering JavaScript by Venkat Subramaniam
- Spring Tips – Bootiful Alibaba
- Spring Tips – RSocket
- Metrics for the Masses
- Serverless Patterns and Anti-patterns
- Tensorflow and deep learning without a PhD
- OAuth 2.0 and OpenID Connect in plain English
- gRPC vs REST
- Architecting for cloud native data – Data Microservices done right using Spring Cloud
- Detecting outliers and anomalies in realtime at Datadog
- Netflix Play API – An Evolutionary Architecture
- Erik Demaine – Algorithms Meet Art, Puzzles, and Magic
- Monads, in my Python
- Writing IntelliJ Plugins for Kotlin
- Dynamic Programming – Richard Buckland
- Best Practices for Unit Testing in Kotlin
- Reactive DDD—When Concurrent Waxes Fluent
- Spring Tips – The Reactor Context
Stanford cs231n
- Assignments
- Lecture 1– Introduction to Convolutional Neural Networks for Visual Recognition
- Lecture 2 – Image Classification
MIT Deep Learning
MIT Introduction to Deep Learning 6.S191
Introduction to Computational Thinking and Data Science
- Course page
- Lecture 1 – Introduction to Optimization Problems
- Lecture 2 – Optimization Problems
- Lecture 3 – Graph-theoretic Models
MIT 6.006 Introduction to Algorithms