Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/mukeshmithrakumar/missing-semester

The Missing Semester of My CS Education
https://github.com/mukeshmithrakumar/missing-semester

beam distributed-systems microservice redpanda rust streaming

Last synced: about 2 months ago
JSON representation

The Missing Semester of My CS Education

Awesome Lists containing this project

README 

          
# The Missing Semester of My CS Education



## General Collection 



Following is a list of general interesting collections, I may or may not be working on any of these. Specific folders means active or planned work.



**Finance**

* [Financial Data Engineering](https://learning.oreilly.com/library/view/financial-data-engineering/9781098159986/)

* [Analyzing Time Series and Sequential Data Specialization](https://www.coursera.org/specializations/time-series-sequential-data)

* [Trading Strategies in Emerging Markets Specialization](https://www.coursera.org/specializations/trading-strategy)

* [Machine Learning and Reinforcement Learning in Finance Specialization](https://www.coursera.org/specializations/machine-learning-reinforcement-finance)



**Data Engineering**

* [Data Engineer Learning Path in GCP](https://www.cloudskillsboost.google/paths/16)

* [Redpanda University](https://www.redpanda.com/university)

* [feast](https://docs.feast.dev/)

* [RAPIDS: GPU Accelerated Data Science](https://rapids.ai/)

* [Accelerate Data Science Workflows with Zero Code Changes](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-DS-03+V1)

* [Accelerating End-to-End Data Science Workflows](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-01+V2)

* [Analyzing and Visualizing Large Data Interactively using Accelerated Computing](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-05+V1)

* [Best Practices in Feature Engineering for Tabular Data With GPU Acceleration](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-06+V1)

* [Synthetic Tabular Data Generation Using Transformers](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-09+V1)

* [Designing Data-Intensive Applications](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/)

* [Data Engineering Design Patterns](https://learning.oreilly.com/library/view/data-engineering-design/9781098165826/)

* [Designing Event-Driven Systems](https://learning.oreilly.com/library/view/designing-event-driven-systems/9781492038252/)

* [Software Architecture Patterns for Big Data](https://www.coursera.org/learn/software-architecture-patterns-for-big-data)

* [Streaming Systems](https://learning.oreilly.com/library/view/streaming-systems/9781491983867/)

* [Streaming Databases](https://learning.oreilly.com/library/view/streaming-databases/9781098154820/)



**GCP/Platform**

* [Getting Started with Terraform for Google Cloud](https://www.coursera.org/learn/getting-started-with-terraform-for-google-cloud)

* [Infrastructure as Code with Terraform](https://www.linkedin.com/learning/paths/infrastructure-as-code-with-terraform?u=36492188)



**General**

* [Designing Distributed Systems, 2nd Edition](https://learning.oreilly.com/library/view/designing-distributed-systems/9781098156343/)

* [Software Architecture Patterns for Big Data](https://www.coursera.org/learn/software-architecture-patterns-for-big-data)

* [UvA Deep Learning Tutorials](https://uvadlc-notebooks.readthedocs.io/en/latest/index.html)

* [Computer Architecture](https://www.coursera.org/learn/comparch)

* [Introduction to Operating Systems Specialization](https://www.coursera.org/specializations/codio-introduction-operating-systems)

* [High-Performance and Parallel Computing Specialization](https://www.coursera.org/specializations/high-performance-parallel-computing)

* [CS149 Parallel Computing](https://gfxcourses.stanford.edu/cs149/fall23), [video](https://youtube.com/playlist?list=PLoROMvodv4rMp7MTFr4hQsDEcX7Bx6Odp&si=UADWlntDE7hdraju)

* [I/O-efficient algorithms](https://www.coursera.org/learn/io-efficient-algorithms)

* [Data Structures and Performance](https://www.coursera.org/learn/data-structures-optimizing-performance)



**GCP**

* [Data Engineering, Big Data, and Machine Learning on GCP Specialization](https://www.coursera.org/specializations/gcp-data-machine-learning)



**LLM Related**

* [Natural Language Processing for Semantic Search](https://www.pinecone.io/learn/series/nlp/)

* [Faiss: The Missing Manual](https://www.pinecone.io/learn/series/faiss/)

* [LLM Embeddings Explained: A Visual and Intuitive Guide](https://huggingface.co/spaces/hesamation/primer-llm-embedding)

* [The Ultra-Scale Playbook: Training LLMs on GPU Clusters](https://huggingface.co/spaces/nanotron/ultrascale-playbook)

* [DevOps-Projects](https://github.com/NotHarshhaa/DevOps-Projects)

* [LLM Course](https://github.com/mlabonne/llm-course)



**Generals**

* [School of SRE](https://linkedin.github.io/school-of-sre/)

* [Microservices Patterns](https://learning.oreilly.com/library/view/microservices-patterns/9781617294549/)

* [gRPC Microservices in Go](https://learning.oreilly.com/library/view/grpc-microservices-in/9781633439207/)

* [Software Engineering at Google](https://abseil.io/resources/swe-book)

* [System Design: The complete course](https://kps.hashnode.dev/system-design-the-complete-course)

* [The Missing Semester of Your CS Education](https://missing.csail.mit.edu/)

* [Designing Distributed Systems](https://learning.oreilly.com/library/view/designing-distributed-systems/9781491983638/)



**Algorithms**

* [Advanced Algorithms and Data Structures](https://learning.oreilly.com/library/view/advanced-algorithms-and/9781617295485/)



**Rust**

* [Rust Programming](https://www.risein.com/courses/rust-programming)

* [Programming with Rust Specialization](https://www.coursera.org/specializations/programming-with-rust)



**JAX**

* [Jaxformer: Scaling Modern Transformers](https://jaxformer.com/)



**ML Hardware & Systems**

* [Stanford MLSys Seminar](https://mlsys.stanford.edu/)

* [Machine Learning Systems](https://ucbrise.github.io/cs294-ai-sys-sp22/)



**ML Compilers**

* [Compilers for Machine Learning](https://www.c4ml.org/c4ml2019)

* [Machine Learning Compiler](https://mlc.ai/)

* [MLIR and MLIR Components Talks](https://mlir.llvm.org/talks/)

* [Machine Scheduler in LLVM - Part II](https://myhsu.xyz/llvm-machine-scheduler-2/)



**NVIDIA Triton**

* [NVIDIA Dynamo Platform](https://developer.nvidia.com/dynamo)



**CUDA**

* [CUDA Mastery](https://tailoredread.com/book/cuda-mastery-advanced-techniques-high-performance-gpu-621c9c8e8c6d)

* [NVIDIA Self-Paced Courses](https://www.nvidia.com/en-us/training/self-paced-courses/)

* [GPU Programming Specialization](https://www.coursera.org/specializations/gpu-programming)

* [Parallel and High Performance Computing](https://learning.oreilly.com/library/view/parallel-and-high/9781617296468/)



**Hacking**:

* [Root Me Website](https://www.root-me.org/?lang=en)



**Linux**

* [pwn.dojos](https://pwn.college/dojos)

* [Introduction to Linux – Full Course for Beginners](https://youtu.be/sWbUDq4S6Y8?si=NC2qXNMRxT9wYc_k)

* [Linux Networking: How The Kernel Handles A TCP Connection](https://youtu.be/ck4WvYM9V4c?si=g9TnqKJpL_s3Aazb)

* [Rearchitecting Linux Storage Stack for µs Latency and High Throughput](https://www.usenix.org/conference/osdi21/presentation/hwang)

* [glibc Heap Series](https://youtube.com/playlist?list=PLZQ4UbPWTlon-F9l6KAlAjxeZsqNmxM5K&si=YvYeOg3lyX5F_Fd6)

* [What Makes System Calls Expensive: A Linux Internals Deep Dive](https://blog.codingconfessions.com/p/what-makes-system-calls-expensive)



**Containers**:

* [Fork in the Road: Reflections and Optimizations for Cold Start Latency in Production Serverless Systems](https://www.usenix.org/conference/osdi25/presentation/chai-xiaohu)

* [Fast, lazy container loading in modal.com by Jonathon Belotti](https://youtu.be/SlkEW4C2kd4?si=KkbJDxvku2Vv-yxr)

* [How Modal built their own container runtime, file system, GPU resource solver, and more](https://youtu.be/pLBxrY8RX6w?si=827jACa2wVWnRoKm)



**Compiler**

* [A Gentle Introduction to LLVM IR](https://mcyoung.xyz/2023/08/01/llvm-ir/)

* [Triton Conference 2024: Morning Session](https://youtu.be/NZz5sczZ_30?si=izn4UUmNmSlghvt6)

* [Flash Attention derived and coded from first principles with Triton (Python)](https://youtu.be/zy8ChVd_oTM?si=eHjBiBMOcqSUHGr_)

*