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21 Lessons, Get Started Building with Generative AI
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
A unified framework for 3D content generation.
Collection of notebooks about quantitative finance, with interactive python code.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
An educational AI robot based on NVIDIA Jetson Nano.
Quantitative analysis, strategies and backtests
Machine Learning University: Accelerated Natural Language Processing Class
Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
Digital Signal Processing - Theory and Computational Examples
This is the code for "I Built a Sports Betting Bot with ChatGPT" by Siraj Raval on Youtube
High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.
NVIDIA cuOpt examples for decision optimization
FPGA-based neural network inference project with an end-to-end approach (from training to implementation to deployment)
This repo provides a managed SageMaker jupyter notebook with a number of notebooks for hands on workshops in data lakes, AI/ML, Batch, IoT, and Genomics.
Course Repository - TinyML - Machine Learning for Embedding Devices
Notebooks and code for Neuromorphic Hardware Workshop at ISFPGA 2024.
Tutorial for surrogate gradient learning in spiking neural networks
A simple from scratch implementation of a Spiking-Neural-Network with STDP in Python which is beeing trained on MNIST.
This repository serves as a container for material around the Brian simulator, such as presentations and tutorials.
Notebooks illustrating the use of Norse, a library for deep-learning with spiking neural networks.
Code to accompany our paper "The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks"
Fully automated pipeline for static code graph analysis
Benford law helps in detecting the irregularity in a set of numbers. It can be used to detect fraud in image forensics(detecting whether the image is real or fake) or it can also be used to analyze…
Credit Risk Analysis with Machine Learning