Yacine Mahdid Courses

FractalNet Deep Neural Network Explained

Explore FractalNet, an alternative to residual networks, through paper analysis and PyTorch implementation. Learn about fractal expansion, drop path, and performance comparisons with ResNet.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Practical Guide to Implementing AI Projects in Business

Master practical steps for implementing AI projects in business settings, from problem identification and data structuring to solution iteration and deployment.

• YouTube
• 10 minutes
• On-Demand
• Free Video

Backpropagation with Automatic Differentiation from Scratch in Python

Master automatic differentiation and backpropagation by building PyTorch's autograd system from scratch in Python with hands-on implementation of core operations.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Reduced Row Echelon Form (RREF) Algorithm From Scratch in Python

Learn how to implement the Reduced Row Echelon Form (RREF) algorithm from scratch in Python, with a step-by-step breakdown of the transformation process for augmented matrices.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Masked Self-Attention from Scratch in Python

Dive into masked self-attention algorithm implementation in Python with numpy, covering theory and step-by-step coding from QKV computation to output generation, essential for understanding LLM pre-training.

• YouTube
• 14 minutes
• On-Demand
• Free Video

What the Heck is Artificial Immune System - AI Paper Review

Explore the fascinating field of artificial immune systems and discover its relevance in modern AI through paper analysis and hyperdimensional computing connections.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

KL Divergence Implementation in DeepSeek R1 - A Deep Learning Tutorial

Dive into KL divergence implementation in DeepSeek R1, exploring mathematical foundations, Monte Carlo estimation, and practical benchmarking for deep learning applications.

• YouTube
• 20 minutes
• On-Demand
• Free Video

DeepSeek R1 Theory Overview - From GRPO to Reinforcement Learning and Supervised Fine-Tuning

Dive into a comprehensive breakdown of DeepSeek R1's architecture, exploring its training pipeline from GRPO and reinforcement learning to supervised fine-tuning and neural reward modeling.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Reinforcement Learning with Verifiable Rewards - RLVR Environments for LLMs

Explore Reinforcement Learning with Verifiable Rewards (RLVR) environments for LLMs, covering the complete workflow from policy rollouts to rubrics using the verifiers library.

• YouTube
• 27 minutes
• On-Demand
• Free Video

How to Build RuneScape for RL Agent - Neural MMO v3 with PufferLib 2.0

Dive into building MMO environments for reinforcement learning agents with Dr. Joseph Suarez, exploring PufferLib 2.0 and Neural MMO v3.0 development insights.

• YouTube
• 1 hour 29 minutes
• On-Demand
• Free Video

AdamW Optimizer from Scratch in Python

Discover how to implement the state-of-the-art AdamW optimizer from scratch, understanding its unique weight decay regularization that outperforms traditional Adam + L2.

• YouTube
• 20 minutes
• On-Demand
• Free Video

Continuous Thought Machine Deep Dive - Temporal Processing and Neural Synchronisation

Explore Continuous Thought Machines, a novel neural architecture with temporal processing through decoupled ticks, featuring hands-on demos and experiments across vision tasks.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

Adam Optimizer from Scratch in Python

Discover how to implement the Adam optimizer from scratch using Python and numpy, covering theory, formulas, and practical coding for deep neural network training.

• YouTube
• 15 minutes
• On-Demand
• Free Video

MiniMax-01 Theory Overview - Lightning Attention + MoE + FlashAttention Optimization

Explore MiniMax-01's architecture featuring Lightning Attention, MoE, and FlashAttention optimizations that enable a 4M token context window and 456B parameters, outperforming Llama 3.1 and challenging Claude in benchmarks.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Introduction to AI Agents - Theory and Code

Explore AI agents through theory and practical code examples, covering definitions, workflows, architectures, and best practices for implementing effective non-autonomous agentic systems.

• YouTube
• 36 minutes
• On-Demand
• Free Video