Machine Learning Concepts

Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.

136
Concepts Explained
27
Topics Covered

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8 concepts

Attention Mechanisms

Understanding attention mechanisms in neural networks, from basic concepts to advanced implementations.

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17 concepts

Deep Learning Fundamentals

Core concepts and building blocks of deep neural networks.

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🚀
7 concepts

GPU Computing

Graphics processing units and parallel computing concepts.

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🧮
8 concepts

Memory Systems

Memory architectures, management, and optimization in AI and computing systems.

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5 concepts

Performance Optimization

Techniques and strategies for optimizing AI and computing performance.

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18 concepts

C++ Programming

Modern C++ concepts, best practices, and advanced programming techniques.

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🌲
2 concepts

Data Structures

Fundamental data structures and their implementations with interactive visualizations.

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🐍
9 concepts

Python Internals

Deep dive into CPython internals, from bytecode compilation to memory management and the GIL.

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📐
5 concepts

Mathematics for ML

Essential mathematical concepts for machine learning with interactive visualizations.

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🐧
19 concepts

Linux & Operating Systems

Deep dive into Linux internals, boot process, kernel concepts, and system architecture.

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5 concepts

Networking & Protocols

Client-server communication patterns, from polling mechanisms to real-time WebSocket connections.

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4 concepts

Multimodal AI

Vision-language models, alignment techniques, and the fundamental challenges of multimodal learning.

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18 concepts

Embeddings & Vector Search

Dense and sparse embeddings, quantization techniques, and advanced retrieval methods for semantic search.

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5 concepts

Graph Theory & Neural Networks

Graph algorithms, neural networks on graphs, and fundamental graph theory concepts with interactive visualizations.

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4 concepts

Large Language Models

Architecture, training, and optimization techniques for large language models and transformers.

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