A Coding Implementation to Build and Train Advanced Architectures with Residual Connections, Self-Attention, and Adaptive Optimization Using JAX, Flax, and Optax
In this tutorial, we explore how to build and train an advanced neural network using JAX, Flax, and Optax in an efficient and modular way. We begin by designing a deep architecture that integrates residual connections and self-attention mechanisms for expressive feature learning. As we progress, we implement sophisticated optimization strategies with learning rate scheduling, gradient clipping, and adaptive weight decay. Throughout the process, we leverage JAX transformations such as jit, grad, and vmap to accelerate computation and ensure smooth training performance across devices.
Check out the FULL CODES here: github.com/Marktechpost/AI-T…
Tutorial: marktechpost.com/2025/11/10/…#machinelearningprojects#machinelearningtutorial#machinelearningcode#machinelearningforbeginners#DataScientist#datasciencetutorials
ALT Simon (he/him), an R developer and data scientist. I build tools for data scientists at Posit PBC (formerly RStudio).🐛
focused on helping R users get the most out of LLMs:
tidyverse/vitals: LLM evaluation
posit-dev/mcptools: Model Context Protocol (MCP) servers and clients in R
posit-dev/btw: easily provide context on R stuff to LLMs
simonpcouch/gander: high-performance, low-friction chat for data science
simonpcouch/chores: an extensible collection of LLM assistants
simonpcouch/predictive: an agentic frontend for predictive modeling with tidymodels
simonpcouch/kapa: RAG-based search via the kapa.ai API