Hong Jing is a data scientist & Alibaba PhD student, write about Machine Learning & Neuroscience.
Publication
i am trask
The i am trask site is a machine learning craftsmanship blog with articles about the field.
About i am trask
i am trask is a personal blog about machine learning craftsmanship, publishing articles and tutorials on the subject. Posts shown on the homepage include a Python walkthrough for a peer-to-peer AI that answers messages using local data, a tutorial on encrypted deep learning, a piece on homomorphic encryption and deep learning for surveillance, and an article on DeepMind's synthetic gradients for training without backpropagation. Other posts cover broader topics such as randomness and unpredictability. The site is English-language and read in a browser; the listing notes no pricing information.
Key features
- Machine learning articles and tutorials in blog format
- Python walkthrough for a decentralized, peer-to-peer messaging AI
- Tutorial on encrypted deep learning
- Article on homomorphic encryption with deep learning for surveillance
- Explainer on synthetic gradients and training without backpropagation
- Archive links to older posts
Best for: Readers and developers who want essays and Python-oriented tutorials on applied machine learning topics such as encrypted learning and decentralized AI.
Good for
- Covers specialized machine learning topics such as encrypted deep learning and synthetic gradients
- Includes tutorial-style posts with Python examples
- Published openly on the web in English
Watch out for
- No pricing, licensing, or subscription information is stated
- Listed post dates range from 2017 to 2026, with no stated publishing schedule
i am trask at a glance
- Official website
- iamtrask.github.io
- Category
- Search & Reference › Articles & Essays
- Type
- Publication
- Platforms
- Web
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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i am trask FAQ
What kinds of posts appear on the blog?
Posts combine essays and tutorials on machine learning craftsmanship, including encrypted deep learning, synthetic gradients, decentralized AI in Python, and more general topics such as randomness.
Do any posts include code?
Yes. For example, one post walks through a peer-to-peer AI in 50 lines of Python, and others are framed as tutorials, such as the encrypted deep learning tutorial.
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