Courses on Srikanth Cherla

Completed Generative AI with LLMs Course on Coursera

Thu, 01 Aug 2024 00:00:00 +0000

I recently completed a Coursera foundation course on Large Language Models — my first structured learning in a while. The gap was mostly down to becoming a parent, which makes carving out time for professional development and blogging considerably harder.

Since April 2023 I’ve been working on Muse at Unity — an LLM-driven AI assistant for Unity developers that has evolved from a web-based chat interface into a deeply integrated Editor tool capable of analysing your project and performing contextual tasks.

Completed Learn TypeScript Course on Codecademy

Tue, 03 May 2022 00:00:00 +0000

I returned from paternity leave to find the team had shifted from Unity’s C# codebase to TypeScript. Having just finished the C# course on Codecademy in December, I was glad to find they had a TypeScript course too.

Prior experience with Python’s mypy type-checker made TypeScript’s type system feel familiar — the conceptual leap was smaller than expected. I also found useful parallels in package management and scripting approaches between languages.

Completed Learn C# Course on Codecademy

Sat, 08 Jan 2022 00:00:00 +0000

I’d been at Unity for over a year without ever properly learning C#. Advanced Unity tutorials weren’t the right entry point — too much assumed knowledge. During a month-long trip to India in December 2021 I went back to basics with Codecademy’s beginner C# course.

The browser-based editor wasn’t enough for me. I set up a local environment with Mono, Dotnet, Omnisharp, and Vim, and made sure to type every piece of code myself — even the repetitive parts like imports and base classes. Repetition was the key.

Completed Practical Reinforcement Learning on Coursera

Sat, 01 May 2021 00:00:00 +0000

Reinforcement Learning is one of the most interesting topics in computer science and ML. I started with the canonical textbook — Reinforcement Learning: An Introduction by Sutton & Barto — which is a fantastic read, though some topics took multiple passes to properly absorb.

After working through Dynamic Programming, Monte-Carlo methods, and Temporal Difference learning, I wanted practical experience and turned to Coursera’s Practical Reinforcement Learning course.

Honest review: it was frustrating. The course tried to cover too much ground too quickly, assignment instructions were minimal and error feedback sparse, and by the halfway point I was no longer enjoying it. I pushed through to the end.

Completed Game Design Foundations on LinkedIn Learning

Sun, 22 Nov 2020 00:00:00 +0000

After joining Unity I needed to get up to speed on game design. I’d moved away from gaming as a consumer over the years to focus on ML and music technology, so coming in fresh to the industry I wanted a solid foundation.

I found a three-part course called Game Design Foundations taught by Brenda Romero on LinkedIn Learning and completed it over about two and a half weeks. It gave me the vocabulary — terms like “core loop” and “lootbox” that colleagues were using — and helped me start to see how my ML background could contribute to game design problems.

Completed Programming Languages (Part B) on Coursera

Sat, 06 Jun 2020 00:00:00 +0000

Completed the second of three parts of Dan Grossman’s Programming Languages course from the University of Washington on Coursera. Where Part A used Standard ML, Part B uses Racket — exploring dynamically typed languages and culminating in implementing a simple language interpreter.

Part A gave me a solid foundation in functional programming and formalised a lot of intuitions I’d built up informally. I’ll share fuller reflections once I’ve finished Part C and had a chance to look back at the whole series.

Completed Programming Languages (Part A) on Coursera

Mon, 09 Mar 2020 00:00:00 +0000

Completed the first part of Dan Grossman’s Programming Languages specialisation from the University of Washington on Coursera, scoring 98%. The course uses Standard ML to teach functional programming — pattern matching, function closures, partial application, currying, mutual recursion.

Beyond the technical content, what stood out was the emphasis on programming style and code quality as part of the evaluation criteria. The assignments were rigorous and the handouts were thorough. An excellently designed course, even if challenging.

Completed Recommender Systems Specialisation on Coursera

Thu, 25 Apr 2019 00:00:00 +0000

After leaving Jukedeck I completed the four-course Recommender Systems specialisation from the University of Minnesota on Coursera:

Introduction to Recommender Systems: Non-personalised and Content-based
Nearest Neighbour Collaborative Filtering
Recommender Systems: Evaluation and Metrics
Matrix Factorisation and Advanced Techniques

Completed in about a month at a leisurely pace. Very well-taught — the coursework used spreadsheet-based implementations to make the algorithms tangible before diving into code. Content-based filtering, item-item and user-user collaborative filtering, matrix factorisation — all well covered.

Completed Machine Learning with Big Data on Coursera

Sat, 17 Mar 2018 00:00:00 +0000

Completed UCSD’s Machine Learning with Big Data on Coursera with a 98.9% mark. The ML theory was introductory — a good refresher on Naive Bayes, Decision Trees, and k-Means, but nothing new. The real value was the hands-on introduction to KNIME and Spark ML applied to real datasets.

Together with the previous course this was more practically focused than the earlier modules in the specialisation, which is what I was after.

Completed Big Data Integration and Processing on Coursera

Sat, 17 Feb 2018 00:00:00 +0000

Completed UCSD’s Big Data Integration and Processing on Coursera with 97.7%. Covered MongoDB for querying JSON data, Pandas for data analysis, and various Spark technologies — Spark SQL, Spark Streaming, Spark MLlib, Spark GraphX. A hands-on exercise involved analysing tweets using both MongoDB and Spark.

The specialisation as a whole is fairly introductory — broad coverage rather than depth — but valuable for building awareness of the ecosystem and knowing where to start when you need to apply these tools.

Completed Andrew Ng's Convolutional Neural Networks on Coursera

Sun, 03 Dec 2017 00:00:00 +0000

Completed Andrew Ng’s Convolutional Neural Networks course — the third in his Deep Learning specialisation — with 100%. This was the most genuinely new material for me; I’d only skimmed a couple of papers on CNNs and never properly implemented one.

The course is excellent. Highlights: 1D, 2D, and 3D convolutions explained clearly and in depth; coverage of VGGNet, InceptionNet, and Network-in-Network architectures; applications including object recognition, face recognition, and Neural Style Transfer. The programming assignments were engaging and moderately challenging, and the reading list was valuable.

Completed Big Data Modeling and Management Systems on Coursera

Mon, 27 Nov 2017 00:00:00 +0000

Completed the first course in UCSD’s Big Data specialisation — Big Data Modeling and Management Systems — with a perfect score. Broad coverage of relational databases, big data management systems, and various processing alternatives. The content was somewhat superficial on individual topics but useful for building a mental map of the ecosystem.

The peer-graded capstone (a relational database design for a game) had poorly defined objectives and evaluation criteria — a weak ending to an otherwise reasonable course. Looking forward to the more hands-on exercises promised in later courses in the specialisation.

Completed Introduction to Big Data on Coursera

Mon, 23 Oct 2017 00:00:00 +0000

Completed UCSD’s Introduction to Big Data on Coursera with 98.9%. The course was light — definitions, history, big data jargon, and very basic principles. The section on the Hadoop ecosystem was new to me and included a hands-on Hadoop assignment which I found worthwhile.

Easy course, but a necessary first step in the specialisation. Looking forward to the remaining courses.

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Completed Andrew Ng's Improving Deep Neural Networks on Coursera

Sun, 01 Oct 2017 00:00:00 +0000

Completed Andrew Ng’s Improving Deep Neural Networks — the second in his Deep Learning specialisation — with 100%. Much of the material was familiar from my ML background, but several sections were genuinely valuable: the detailed treatment of optimisation techniques (exponential moving averages, Momentum, RMSProp, Adam), batch normalisation, and dropout.

On to the Convolutional Neural Networks course next.

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Completed Andrew Ng's Structuring Machine Learning Projects on Coursera

Mon, 28 Aug 2017 00:00:00 +0000

Completed Andrew Ng’s Structuring Machine Learning Projects with 96.7%. Reasonably familiar material given my background, but a few useful insights.

The lectures on Transfer Learning, Multitask Learning, and End-to-End ML were too brief to be immediately useful — they’d need to be followed up with deeper reading. But the practical advice and real-world scenario exercises were valuable, and I wish there were more of them (perhaps as optional material).

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