Yacine Mahdid Courses

Learning Machine Learning Efficiently

1 review

Discover an efficient approach to learning machine learning, exploring the landscape, optimal paths, and practical strategies for mastering this complex field.

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

AI Path Finding - Easy Search - Raw Problem Solving

Raw problem-solving session tackling an AI path-finding challenge using the Understand, Solve, Code framework, with real-time debugging and solution implementation.

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

Shannon Entropy from Theory to Python

Learn to quantify information in sequences using Shannon Entropy. Explore the theory, formula, and Python implementation of this powerful information theory metric for data analysis and comparison.

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

Kolmogorov Complexity for DNA Sequences Analysis in Python

Learn to analyze DNA sequence complexity using Kolmogorov Complexity and Python. Covers theoretical background, Lempel-Ziv algorithm, and practical implementation with code walkthrough and visualization.

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

Distance Metrics Explained and Visualized in Python

Explore five common distance metrics in Python, including theory, code, and visualizations. Learn how to choose and apply the right metric for better results in data analysis and machine learning.

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

How to Read Math as a Software Engineer

Learn to interpret mathematical formulas as a software engineer, reducing anxiety and enhancing coding skills through practical examples and step-by-step guidance.

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

How to Easily Implement Any Data Structures

Learn effective techniques for implementing data structures with C++ examples, improving your understanding and skills in algorithms and data structures.

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

Orthogonalized Minimum Spanning Tree and Their Application for Human Brain Mapping - Paper Review

Explore Orthogonalized Minimum Spanning Trees for brain connectivity analysis. Learn about data-driven thresholding methods, their application in human brain mapping, and practical implementation through code examples.

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

Neural Style Transfer - From Theory to Pytorch Implementation

Explore neural style transfer using PyTorch, from theory to implementation. Learn to manipulate photos with deep neural networks, creating stunning AI-generated artwork.

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

GoogLeNet Deep Neural Network Explained - Inception V1 Architecture and Implementation

Explore GoogLeNet's 22-layer deep neural network architecture, its Inception modules, and Pytorch implementation. Learn about its design principles and performance on image classification tasks.

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

Stochastic Depth for Neural Networks - Implementation and Analysis

Explore stochastic depth in neural networks: a regularization method for residual networks that enhances training speed and test performance. Includes methodology explanation and PyTorch implementation.

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