What Machine Learning and Neuroscience Topics Does Jingles Write About?

Jingles (Hong Jing) writes about machine learning and neuroscience, and the site's article list shows a mix of conceptual explainers, research-adjacent posts, and practical how-tos. Representative topics include reinforcement learning agents, transformers, convolutional layers, GANs, language models, and signal decoding, alongside hands-on posts such as getting started in ML and setting up LaTeX. Readers who want to browse by subject can use the site's tag-based article browsing.

Machine Learning Topics

The machine learning coverage leans toward explaining how core methods work and where they are applied. From the article titles on the site, representative themes include:

• Reinforcement learning: coordinating multiple RL agents in the game Overcooked.
• Optimization: why learned optimizers outperform standard optimizers like Adam.
• Deep learning fundamentals: how convolutional layers work in neural networks.
• Sequence modeling: an illustrated guide to Transformers and three types of sequence prediction problems.
• Language models: state-of-the-art language models in 2020.
• Generative models: whether GANs can be used beyond generating art.
• 3D object understanding: an introduction to Objectron as an "ImageNet" for 3D object understanding.
• Evaluation: how to evaluate whether AI is intelligent.

These titles suggest a reader can move from foundational explanations (convolutional layers, sequence problems) to model families (Transformers, GANs, learned optimizers) and then to application-oriented discussions (RL agents, Objectron, evaluation).

Practical and Getting-Started Posts

The site also includes posts aimed at people entering or working in the field, such as "7 ways to get started into ML, ranked from easiest to hardest" and a setup guide for LaTeX on the Atom editor. These are useful if you want orientation rather than a research deep dive.

Neuroscience and AI Topics

Neuroscience appears both as a subject in its own right and as a lens on AI. Representative titles include:

• Decoding brain signals with machine learning and neuroscience.
• The relationship between AI and neuroscience, framed as a "fascinating relationship."
• An introduction to signals, which supports the signal-processing side of brain-data work.
• Human cognition as a model: a post noting that "babies are awesome… Humans are the OG neural net."

The through-line is the exchange between biological and artificial systems: how brain signals can be analyzed with ML methods, and what neuroscience suggests about intelligence and learning.

Applied and Cross-Disciplinary Posts

Beyond the two core areas, the article list shows applications and adjacent skills:

• Computer vision in medicine: how radiologists used computer vision to diagnose COVID-19.
• AI during the pandemic: how artificial intelligence played a critical role in the pandemic.
• Deepfakes: creating realistic deepfakes in 5 minutes on Colab.
• Data storytelling: what 200,000 years of humanity taught us about data storytelling.
• Everyday applications: improving a holiday itinerary with machine learning.

How to Explore the Site

The site organizes content through a blog, a showcase of projects, published work, and an about page, with links to Medium, LinkedIn, and GitHub. The article list is paginated (pages 1, 2, 3), and a "Browse articles by tags" entry point lets you jump to a specific subject rather than reading chronologically.

The site is a personal blog by a data scientist and PhD student, so the posts read as explanations and explorations rather than a structured curriculum; use the tags and pagination to locate the topics above.