π€ Implement sim2sim training based on Unitree_rl_gym, enabling motion and policy modification for enhanced robotic behavior in Beyondmimic.
A project to implement a subset of the functionality of Ruby's Enumerable.
ALX Higher level programming projects are solutions to solve complex challenges
Classification (Pattern Recognition) algorithm development with Bayesian, Anti-Bayesian, Decision Tree and Dependence Tree concepts. [IEMIS 2020, Springer AISC]
Exploration of brain-inspired computing architectures through computational neuroscience models, focusing on point neuron dynamics and spiking neural networks (SNNs) using Nengo. Bridges concepts from neuroscience, computer science, and electrical engineering.
A project showcasing a scheduling algorithm to optimize warehouse robot coordination for efficient product delivery and order fulfillment.
π¨ Explore random, Nvidia-inspired projects in this unique collection, showcasing creativity and innovation for developers and tech enthusiasts alike.
A curated collection of hands-on notebooks exploring core machine and deep learning concepts. Each notebook focuses on a specific topic - from linear models and foundational elements like broadcasting and autograd to advanced tasks such as custom layers, transfer learning, sequence modeling with RNNs, and representation learning with autoencoders.
π οΈ Implement efficient deformable grid sampling operations with bilinear interpolation in PyTorch, enhancing deep learning models for dense vision tasks.
Quantitative Analytics Suite A hands-on Python project inspired by JPMorganβs quantitative research challenges. It covers four core modules: natural gas price forecasting, storage contract pricing, credit risk modeling (PD & expected loss), and FICO score quantization using DP and likelihood optimization.
Developed a custom clustering algorithm to analyze wine data without traditional machine learning. The project standardizes features and employs mathematical formulas using NumPy to identify distinct clusters, offering insights into wine sample groupings and their characteristics.
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β‘ Optimize heterogeneous reinforcement learning with GEPO for decentralized AI training, tackling policy staleness in large models effectively.
π Streamline social media analysis for sensitive roles with evidence-driven insights, leveraging AI for fast assessments and detailed reporting.
π Explore a curated collection of research on Label-Free Reinforcement Learning with Verifiable Rewards for enhancing Large Language Models.
π Simplify web development with chapel-bwu, a powerful Chapel framework for building fast and scalable web applications.
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