Best of Deep Learning — June 2025

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   Sebastian Raschka·50w

   Coding LLMs from the Ground Up: A Complete Course

   Sebastian Raschka shares a comprehensive video course series on building Large Language Models from scratch using Python and PyTorch. The course covers seven key areas: environment setup, text data preprocessing and tokenization, attention mechanisms implementation, LLM architecture coding, pretraining on unlabeled data, classification fine-tuning, and instruction fine-tuning. The content serves as supplementary material to his book 'Build a Large Language Model (From Scratch)' and emphasizes hands-on learning through implementation rather than using pre-built frameworks.

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   Article

   ByteByteGo·49w

   EP167: Top 20 AI Concepts You Should Know

   A comprehensive overview of 20 essential AI concepts including machine learning, deep learning, neural networks, NLP, computer vision, and transformers. Also covers the AI application stack for building RAG applications, featuring components like large language models, frameworks, vector databases, data extraction tools, and text embeddings. Additionally includes insights into Shopify's tech stack architecture and job opportunities in AI and software engineering.

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   Video

   YouTube·48w

   STOP Taking Random AI Courses - Read These Books Instead

   A comprehensive guide to learning AI and machine learning through structured resources rather than random courses. Covers five key areas: programming fundamentals with Python, mathematics and statistics foundations, traditional machine learning concepts, deep learning and LLMs, and AI engineering for production deployment. Emphasizes practical application over theoretical study, recommending specific books like 'Hands-On ML with Scikit-Learn and TensorFlow' and courses like Andrew Ng's specializations. Highlights the importance of understanding both foundational concepts and modern deployment practices for current AI engineering roles.

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   Article

   Hacker News·50w

   Fine-Tuning LLMs is a Huge Waste of Time

   Fine-tuning advanced LLMs for knowledge injection is counterproductive because it overwrites existing valuable information stored in densely interconnected neurons. Instead of adding knowledge, fine-tuning risks destroying the carefully built ecosystem of an already trained model. Better alternatives include retrieval-augmented generation (RAG), adapter modules like LoRA, and contextual prompting, which inject new information without damaging the underlying model's knowledge base. These modular approaches preserve the integrity of pre-trained networks while achieving the desired knowledge enhancement goals.

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   Article

   ByteByteGo·48w

   EP168: AI Vs Machine Learning Vs Deep Learning Vs Generative AI

   Explains the hierarchical relationship between AI, machine learning, deep learning, and generative AI, with AI as the overarching field and each subsequent term representing more specialized subsets. Covers SQL query execution through database subsystems including transport, query processor, execution engine, and storage engine. Lists 20 key AI agent concepts from basic agents to multi-agent systems and swarm intelligence. Describes RabbitMQ's message broker architecture using exchanges, queues, and bindings for routing messages between producers and consumers.

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   Learn Python·50w

   Data Science Roadmap

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   Video

   Welch Labs·50w

   The F=ma of Artificial Intelligence

   Backpropagation, discovered by Paul Werbos in the 1970s, is the fundamental algorithm that trains virtually all modern AI models including large language models like LLaMA. The algorithm uses calculus and the chain rule to efficiently compute gradients - the slopes of the loss function with respect to each model parameter. These gradients guide the learning process by indicating how to adjust parameters to reduce prediction errors. The explanation demonstrates backpropagation through a simplified GPS coordinate classification model, showing how the algorithm scales from basic linear models to complex neural networks capable of learning intricate patterns in high-dimensional spaces.

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