Tag: Machine Learning

Rocket League reinforcement learning-trained bot

As a passionate gamer, I have been reading about the Rocket League Nexto cheat situation with keen interest (https://kotaku.com/rocket-league-machine-learning-cheating-nexto-bot-1849980593). For those unfamiliar with games, Rocket League is a competitive online game where players control cars to play football. Someone has built a bot trained via reinforcement learning, and offered it as a cheating solution to… Continue reading Rocket League reinforcement learning-trained bot

Timeline of LLM

“Classic quant signals might work, but you can’t explain them; ChatGPT might not work, but it can explain itself. In a sense this is the opposite of a classic “black box” machine-learning investment algorithm.” This is an interesting take by Matt Levine, on the ability of ChatGPT to be an asset manager (https://www.bloomberg.com/opinion/articles/2023-01-26/chatgpt-is-not-much-of-a-pitch-robot). One of… Continue reading Timeline of LLM

Delegating to chatGPT & Stable Difussion

This week, I am jumping on the bandwagon by delegating the task of creating my LinkedIn post to chatGPT (for text) and Stable Diffusion (for image) with the following prompts. ⌨ chatGPT : Can you write an opinion article, discussing your opinion on this topic, “In the future, do you see a shift in companies… Continue reading Delegating to chatGPT & Stable Difussion

Illustrated stable diffusion from Jay Alammar

If you are interested in knowing how Stable Diffusion generates amazing AI arts, but are put off by the steep technical details, This illustrated guide from Jay Alammar should help you, https://jalammar.github.io/illustrated-stable-diffusion/. The guide helpfully breaks down the model into components and substitutes complex equations with simple flowcharts. P/S: I also highly recommend his illustrated… Continue reading Illustrated stable diffusion from Jay Alammar

Short review of Designing Machine Learning System

I have finally finished “Designing Machine Learning Systems” after a few weekends of focus reading. It is one of the rare technical books that I finished in its entirety, and I thoroughly enjoyed it. Just to offer a quick book review below. The book is amazing in the following aspects: ✅Provides a high-level overview of… Continue reading Short review of Designing Machine Learning System

Automatic speech recognition – Whisper OpenAI

Whisper is a recently released transformer-based automatic speech recognition (ASR) model from OpenAI. It can be used for: 🗣Language identification 🗣Voice activity detection 🗣Multi-lingual speech recognition 🗣Multi-lingual speech translation When evaluated on the ESB datasets (including LibriSpeech, Common Voice), Whisper outperformed Conformer RNN-T from NVidia and Wav2Vec2 from Meta. Link to blog: https://openai.com/blog/whisper/Link to repo:… Continue reading Automatic speech recognition – Whisper OpenAI

Data versioning

“Data versioning is like flossing. Everyone agrees it’s a good thing to do, but few do it.” ~ Chip Huyen, Designing Machine Learning Systems Unlike code versioning, it is a lot more difficult to implement data versioning in data science / machine learning projects. It is because of the following reasons: ➡️ Data is often… Continue reading Data versioning

Concept drift vs data drift vs covariate drift

Do you always get confused among concept drift vs data drift vs covariate drift like me? The diagram (from a research paper, https://arxiv.org/abs/1511.03816) provides a clear illustration of the different terms. In summary, concept drift in data refers to changes in environmental conditions that differ from the original environmental conditions under which a model is… Continue reading Concept drift vs data drift vs covariate drift

ML system design – problem definition & consulting

I recently started reading the excellent book called Designing Machine Learning Systems by Chip Huyen. In the first few chapters, the book illustrated very clearly the differing stakeholder expectations of an ML system, by using a restaurant recommendation app as an example. Data scientists / ML engineers ➡️ Want a model that recommends the best… Continue reading ML system design – problem definition & consulting