A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-ar…

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Sensible and minimal starting Emacs configuration. A clean room implementation of a pure vanilla Emacs configuration that is sanely manageable, and practically useful.

Machine learning-Stanford University

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

A huge VHDL library for FPGA development

Neural Network for Pattern Recognition on an FPGA. Project for Education. Video lectures explain training of the network and FPGA implementation with VHDL.

UVM 1.2 port to Python

PicoRV32 - A Size-Optimized RISC-V CPU

Code repository for O'Reilly book

Small SERV-based SoC primarily for OpenMPW tapeout

Python Data Science Handbook: full text in Jupyter Notebooks

Static Timing Analysis Full Course

Open, Modular, Deep Learning Accelerator

Verilog library for ASIC and FPGA designers

STM32 Application for Deep Learning Framework with TVM