Ask HN: How to get back into AI?

• NNfSiX

  46 1,344 0.0 C++

  Neural Networks from Scratch in various programming languages

Have you had a look at https://nnfs.io/ ? I bought the book and am gearing up to start working through it, I would be interested to know your thoughts. Generally I want to chart a personal curriculum from data engineer to practical application of modern AI to real business problems.

• tinygrad

  58 17,800 9.7 Python

  Discontinued You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad] (by geohot)

  

Read all the leading papers, many times, to get a deep understanding, the writing quality is usually pretty low, but the information density can be very high, you'll probably miss the important details the first time.

Most medium and low-quality papers are full of errors and noise, but you can still learn from them.

Get your hands dirty with real code.

I would take a look at those:

https://github.com/geohot/tinygrad

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• whisper.cpp

  187 30,942 9.8 C

  Port of OpenAI's Whisper model in C/C++

• best-of-ml-python

  16 15,302 7.9 Python

  🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

  

For Python, here's a nice compilation: https://github.com/ml-tooling/best-of-ml-python/blob/main/RE...

• course-content

  14 2,587 7.9 Jupyter Notebook

  NMA Computational Neuroscience course

  

The neuromatch computational neuroscience course also seems quite interesting, though maybe less of practical use.

https://compneuro.neuromatch.io/

Recent research like "Relating transformers to models and neural representations of the hippocampal formation" might make it more relevant though (https://arxiv.org/abs/2112.04035v2)

quote from the abstract of that paper:

• score_sde

  6 1,242 0.0 Jupyter Notebook

  Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Suggest a related project