Yacine Mahdid Courses

Sleep Classification with Python Using EEG, Sklearn and MNE - Part 1

Enhance EEG sleep stage classification using Python, Sklearn, and MNE. Expand dataset, add new class, and implement Leave-One-Subject-Out cross-validation for improved results.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

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
• Self-Paced
• Free Video

DenseNet Deep Neural Network Architecture Explained

Comprehensive explanation of DenseNet architecture, including its benefits and implementation in PyTorch. Covers theory, results, and practical coding walkthrough for deep learning enthusiasts.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Img2Vec and UMAP for High-Dimensional Data Visualization

Learn to visualize high-dimensional image data using Img2Vec for embeddings, UMAP for dimensionality reduction, and Bokeh for interactive exploration. Ideal for vision classification projects.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

Intersection over Union (IoU) Explained with PyTorch

Learn to implement Intersection over Union (IoU) for object recognition using PyTorch. Covers theory, formulas, and practical code for bounding boxes and segmentation masks.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

How to Read Math in Deep Learning Papers - A 5-Step Process

Learn a 5-step method to decipher mathematical formulas in deep learning papers, enhancing your understanding and intuition of complex AI concepts.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Structuring Data Science Projects with Docker

Structure data science projects using Docker for seamless model deployment. Learn advanced techniques, explore code, address vulnerabilities, and weigh pros and cons of Docker in data science workflows.

• YouTube
• 13 minutes
• Self-Paced
• Free Video

Log Softmax Explained with Python

Explore log softmax implementation in Python, enhancing numerical stability for machine learning. Gain insights into softmax limitations and practical coding solutions.

• YouTube
• 12 minutes
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video

The Neural Basis of Moral Judgment - Neuro/AI Paper Review

Explore the neural mechanisms underlying moral decision-making through a detailed 2022 research paper review, connecting neuroscience insights to AI alignment challenges.

• YouTube
• 2 hours 16 minutes
• Self-Paced
• Free Video

Muon Optimizer for Dense Linear Layer Explained - Newton-Schulz + Momentum

Discover the Muon optimizer revolutionizing large language models through Newton-Schulz orthogonalization and momentum, with hands-on NumPy implementation and real-world applications.

• YouTube
• 33 minutes
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video