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czephyr/README.md

tldr 📖

Computer Science is super interesting
My dev interests are smooth operations, scaling, observability, System Design and Machine Learning.

  • Studying for my master's degree in Data Science.
  • Working with mostly opensource self-hosted tools
    as R&D DevOps Engineer in the Telecom sector for a non-profit.

What 2024 will bring me (won't accomplish everything probably and its fine!) 🌱

  • Improving at Data Structures and Algorithms
  • Better System Design skills
  • Learning Go [If thats a goals of yours too I suggest starting from learning Go with TDD!] [-]
  • A GCP certification
  • Switching to Pytorch
  • Working with impactful machine learning applications
  • Delve into recommending systems
  • A deeper understanding of GenAI

Some of the stuff I know

A brief list of interesting resources I loved 📚

  • Machine Learning Engineering - Andriy Burkov [-] (A general overview of ML projects)
  • d2l.ai Dive Deep into Deep Learning [-] (Absolutely the best Deep Learning resource out there FWIK)
  • An introduction to Statistical Learning - Springer [-] (suggested reading from the Statistical Learning exam in my masters)
  • Applied Unsupervised Learning with Python [-] (clustering and dimensionality reduction)

  • TCP/IP Guide - Kozierok [-] (If only this had been the suggested reading for my networking exam... ç_ç)
  • CKA training - Mumshad Mannambeth on Coursera [-] (kindly paid for by my company)
  • A Crash Course in Linux Networking - David Guyton [-] (helped me understand iptables)
  • System Design vol.1 - Alex Xu from ByteByteGo [-] (cool real world system design case studies)
  • Observability Engineering [-] (how to use tracing for observability)

  • Principles of Economics - Mankiw [-] (Intro to economics)

  • ...

Stuff I want to eventually come around to 📋

  • How Linux Works - Brian Ward [-]
  • Site Reliability Engineering: How Google Runs Production Systems [-]
  • Machine Learning System Design - Ali Aminian, Alex Xu from ByteByteGo [-]
  • WYAG: Write yourself a Git - Thibault Polge [-]
  • Kubernetes the Hard way - Kelsey Hightower [-]
  • Programming Kubernetes - Hausenblas, Schimanski [-]
  • Distributed Systems for fun and profit [-]
  • Designing Data-intensive applications - Kleppmann [-]
  • Building GPT-2 from scratch - Andrej Karpathy [-]
  • Let's read the Kubernetes source code - Ants Are Everywhere [-]
  • I Heart Logs - Jay Kreps [-]
  • ...

Numbers don't matter but here's what I work with 🛠️

czephyr's WakaTime stats

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  1. serenade serenade Public

    🏥 Platform for medical IoT data consultation.

    JavaScript

  2. kubernetes-the-hard-way kubernetes-the-hard-way Public

    ☸️ Getting to understand Kubernetes, Openstack and Terraform, all at the same time :)

    HCL

  3. ultrasounds_classification ultrasounds_classification Public

    🩻 Design and implementation of a Tensorflow Convolutional Neural Network to classify human joints.

    Jupyter Notebook 1

  4. quarkus-RESTapi quarkus-RESTapi Public

    ☕ Quarkus based restAPI using Hibernate-panache deployed on GKE

    Java

  5. deploy_ml_hackaton deploy_ml_hackaton Public

    🐕 Deployment of an animal photos classification CNN model on GCP Cloud Run

    Jupyter Notebook 1

  6. dsereviews dsereviews Public

    🎓 Webapp for reviewing professors and exams of the master degree.

    Python