I have never encountered a problem that required a variational autoencoder (VAE) to solve. I hope I never will. Still, everyone talks about VAEs. I hear about them so often it feels like everyone ...

This is based on my discussion with ChatGPT. I’ve been thinking about this topic for a while, and what motivates me is the following observation: People with knowledge—such as technical profession...

The classic split between generative and discriminative models was introduced for supervised learning: compare Naïve Bayes (model $p(x, y)$) with Logistic Regression (model $p(y\mid x)$). In conte...

My BLDC Experiment Setup I learned that the joints in robots are mainly using BLDC motor + FOC control. To have a better understanding, I bought two sets of small BLDC + MT6701 magnetic encoder +...

This article is mainly generated by ChatGPT, I have tried many prompts and the results is still not perfrect. However I found it already quite useful to give me a mind map of this field. The Bayes...

After a decade-long pause, I resumed my hobby of building robots this summer holiday. A decade ago, I was playing with Lego NXT and Arduino. This time, I am going pro with STM32 (e.g., following an...

Just before my summer holiday in China, I became interested in the visual neural systems of insects and other species, and how they compare to each other. Along this line of investigation, I asked ...

I read the paper Dissecting Recall of Factual Associations in Auto-Regressive Language Models by Geva et al., and I highly recommend it. If you’re curious about how large language models process in...

In the last post of this series, we tried the instruction-tuned IT model. In this post, we focus on the raw PT model. The following is Table 15 from the Gemma 3 technical report. It compares the p...

In the first post of this series, we examined the specifications of the Gemma 3 model. In this post, we will actually run it and get an intuitive understanding of the inference process. I will assu...