Best of Machine Learning — June 2025

  1. 1
    Article
    Avatar of sebastianraschkaSebastian Raschka·44w

    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.

  2. 2
    Article
    Avatar of bytebytegoByteByteGo·44w

    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.

  3. 3
    Article
    Avatar of swirlaiSwirlAI·45w

    Breaking into AI Engineering in 2025.

    A comprehensive roadmap for becoming an AI Engineer in 2025, covering essential skills from Python fundamentals and LLM APIs to advanced topics like AI agents, RAG systems, and observability. The guide emphasizes learning fundamentals while building practical skills, starting with basic LLM integration and progressing through vector databases, prompt engineering, agentic systems, infrastructure deployment, and security considerations. Key recommendations include mastering FastAPI and Pydantic, understanding different LLM types and structured outputs, implementing RAG with proper data preprocessing, and learning agent design patterns like ReAct and task decomposition.

  4. 4
    Article
    Avatar of tdsTowards Data Science·45w

    How to Design My First AI Agent

    A comprehensive guide to designing AI agents covering model selection, tooling choices, and reliability strategies. Explores different LLM options including OpenAI GPT-4, DeepSeek, Claude, and Mistral, each suited for specific use cases. Discusses infrastructure considerations, frameworks like LangGraph and Pydantic-AI, and security aspects. Emphasizes the importance of structured prompting techniques like Chain-of-Thought and ReAct, output validation, and failure handling to build reliable production-ready agents.

  5. 5
    Video
    Avatar of youtubeYouTube·43w

    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.

  6. 6
    Article
    Avatar of workchroniclesWork Chronicles·43w

    (comic) AI Chronicles: State-of-the-art Output

    A humorous comic strip that satirizes the gap between AI marketing claims of 'state-of-the-art' performance and the actual quality of AI-generated outputs that developers encounter in practice.

  7. 7
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·45w

    48 Most Popular Open ML Datasets

    A comprehensive compilation of 48 widely-used open machine learning datasets organized by domain including computer vision (ImageNet, COCO), natural language processing (SQuAD, GLUE), recommendation systems (MovieLens, new Yambda-5B), tabular data (UCI datasets, Titanic), reinforcement learning (OpenAI Gym), and multimodal learning (LAION-5B, VQA). Each dataset is briefly described with its primary use case and key characteristics, serving as a reference guide for researchers and practitioners selecting appropriate datasets for their ML projects.

  8. 8
    Article
    Avatar of palindromeThe Palindrome·42w

    The 10 Most Important Lessons 20 Years of Mathematics Taught Me

    A mathematician with 20 years of experience shares ten key lessons about learning and mastery. The core insights include the importance of understanding fundamentals before breaking rules, learning through hands-on problem solving rather than passive consumption, and recognizing that there are no shortcuts to expertise. The author emphasizes taking things slow to build deep understanding, tackling complexity one step at a time, and finding the right perspective to solve problems. Other key points include the power of asking questions without shame, the primacy of hard work over talent, and the importance of forging your own path rather than blindly following others' advice.

  9. 9
    Article
    Avatar of tarzzotechTarzzo Tech·45w

    What is MCP (Model Context Protocol)?

    Model Context Protocol (MCP) is a communication standard that enables AI models to interact with external systems and data sources. It provides a structured way for large language models to access and exchange contextual information, improving their ability to provide relevant and accurate responses by connecting them to real-time data and external services.

  10. 10
    Article
    Avatar of bytebytegoByteByteGo·42w

    EP169: RAG vs Agentic RAG

    RAG (Retrieval Augmented Generation) combines information retrieval with large language models, but traditional RAG has limitations in adaptability and real-time processing. Agentic RAG introduces AI agents that make decisions, select tools, and refine queries for more accurate responses. The comparison covers Kubernetes fundamentals including control planes, nodes, and key resources like Pods and Deployments. Six space-efficient data structures are highlighted: Bloom Filter, HyperLogLog, Cuckoo Filter, Minhash, SkipList, and Count-Min Sketch. Database normalization forms from 1NF to 4NF are explained for eliminating redundancy and enforcing data integrity.

  11. 11
    Article
    Avatar of mlmMachine Learning Mastery·44w

    Implementing Vector Search from Scratch: A Step-by-Step Tutorial

    A comprehensive tutorial demonstrating how to build a vector search engine from scratch using Python. Covers the three core steps of vector search: converting text to numerical vectors, calculating similarity using cosine similarity, and retrieving the most relevant results. Includes practical code examples with NumPy and Matplotlib, visualization of vector spaces, and explains the connection to RAG systems. The implementation uses simplified word embeddings and averaging techniques to make concepts accessible while maintaining the fundamental principles of semantic search.

  12. 12
    Video
    Avatar of codinggopherThe Coding Gopher·43w

    99% of Developers Don't Get LLMs

    Large language models work by predicting the next token in a sequence using transformer architecture with self-attention mechanisms. They're trained on massive text datasets to learn patterns, grammar, and relationships between concepts. The transformer processes all tokens simultaneously rather than sequentially, allowing better capture of long-range dependencies. Generation happens through probability distributions over vocabulary, with techniques like temperature and top-k sampling controlling randomness. Models become more capable with scale, exhibiting emergent behaviors not present in smaller versions. Raw models are aligned with human preferences through reinforcement learning with human feedback (RLHF). Despite their fluency, LLMs have significant limitations including hallucination, lack of persistent memory, and sensitivity to input phrasing.

  13. 13
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·43w

    10 MCP, RAG and AI Agents Projects

    A curated collection of 10 advanced AI engineering projects covering MCP-powered applications, RAG systems, and AI agents. Projects include video RAG with exact timestamp retrieval, corrective RAG with self-assessment, multi-agent flight booking systems, voice-enabled RAG agents, and local alternatives to ChatGPT's research features. The repository contains 70+ hands-on tutorials focusing on real-world implementations of LLMs, memory-enabled agents, multimodal document processing, and performance optimization techniques like binary quantization for 40x faster RAG systems.

  14. 14
    Article
    Avatar of mlmMachine Learning Mastery·45w

    10 Python One-Liners That Will Simplify Feature Engineering

    Ten practical Python one-liners for feature engineering tasks including standardization, min-max scaling, polynomial features, one-hot encoding, discretization, logarithmic transformation, ratio creation, low variance removal, multiplicative interactions, and outlier tracking. Each technique uses popular libraries like scikit-learn and pandas to transform raw data into meaningful features for machine learning models.

  15. 15
    Article
    Avatar of syncfusionSyncfusion·43w

    Best 5 Open-Source LLMs for Developers: ChatGPT Alternatives in 2025

    Five powerful open-source large language models offer cost-effective alternatives to ChatGPT for developers in 2025. Llama 3 delivers GPT-3.5-level performance with commercial licensing freedom, while Mistral AI provides exceptional efficiency with smaller parameter counts. Falcon offers flexible model sizes from 7B to 180B parameters, BLOOM supports 46 natural languages for global applications, and Pythia serves as a research-grade suite for AI interpretability studies. These models enable local deployment, complete customization, and freedom from API restrictions, though they require careful consideration of hardware requirements and deployment strategies.

  16. 16
    Article
    Avatar of mlmMachine Learning Mastery·45w

    10 MLOps Tools for Machine Learning Practitioners to Know

    MLOps combines machine learning with DevOps practices to streamline model lifecycle management from training to deployment. Ten essential tools are highlighted: MLflow for experiment tracking, Weights & Biases for visualization, Comet for monitoring, Airflow for workflow automation, Kubeflow for Kubernetes-based pipelines, DVC for data versioning, Metaflow for Python workflows, Pachyderm for data pipelines, Evidently AI for model monitoring, and TensorFlow Extended for complete ML pipelines. These tools address different aspects of MLOps including experiment tracking, workflow automation, data versioning, and model monitoring to help teams build reliable, production-ready machine learning systems.

  17. 17
    Article
    Avatar of theregisterThe Register·43w

    ChatGPT polluted the world forever, like the first atom bomb

    AI researchers warn that ChatGPT's launch in 2022 created a permanent contamination problem for machine learning training data, similar to how nuclear testing contaminated metals. The concern is that AI models trained on synthetic data from other AI models will suffer from 'model collapse,' becoming less reliable over time. This creates competitive advantages for companies with access to pre-2022 'clean' data and may lead to AI monopolies. Proposed solutions include mandatory AI content labeling, federated learning approaches, and government regulation to preserve access to uncontaminated datasets.

  18. 18
    Article
    Avatar of hnHacker News·44w

    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.

  19. 19
    Article
    Avatar of towardsdevTowards Dev·44w

    vLLM: A Quick Start

    vLLM is an open-source library optimized for high-throughput serving of large language models in production. Its core innovation, PagedAttention, manages memory more efficiently by breaking the key-value cache into fixed-size pages instead of contiguous buffers, similar to virtual memory in operating systems. The tutorial covers installation on macOS M1, serving models via OpenAI-compatible API, using the native Python API, and integrating with LangChain for enhanced tooling capabilities.

  20. 20
    Article
    Avatar of ralphnexRalphNex·44w

    I was tired of scam calls, so I built an AI that messes with them 😘

    A developer built an AI-powered call screening service that automatically answers unwanted calls, classifies them as scam or legitimate, and sends WhatsApp summaries for legitimate calls with action items. The service acts like an intelligent voicemail system that can interact with callers and filter out telemarketers while ensuring important calls aren't missed.

  21. 21
    Article
    Avatar of diamantaiDiamantAI·43w

    25 FREE Tutorials to Build Production-Ready AI Agents Fast!

    A collection of 25 free tutorials covering comprehensive AI agent development from orchestration and tool integration to deployment, security, and evaluation. The resource covers production-ready implementation aspects including observability, memory management, multi-agent coordination, and various frameworks, organized into 11 key categories for building real-world AI agents.

  22. 22
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·43w

    Deploy any ML model, RAG or Agent as an MCP server

    LitServe now supports MCP (Model Context Protocol) integration through a dedicated endpoint, allowing any ML model, RAG system, or AI agent to be deployed as an MCP server. This eliminates the need for custom integration code for each application. The implementation involves defining input schemas, setup methods, and inference logic in a simple Python class structure. The article also covers a 4-part MCP crash course and demonstrates deploying a Qwen 3 Agentic RAG system using CrewAI, Firecrawl, and LitServe.

  23. 23
    Video
    Avatar of hitenshowHiten Shah·43w

    Why Meta stole millions of books to train AI

    Meta downloaded 82 terabytes of pirated books from shadow libraries like LibGen to train their Llama AI model, despite legal concerns from engineers. After publishers refused licensing deals deemed too expensive and slow, Meta chose piracy over falling behind competitors like OpenAI and Google. The pirated data improved Llama's performance by 5%, leading to 800 more correct answers. Meta covered their tracks by masking IP addresses and removing copyright tags, while relying on a fair use legal defense strategy shared across the AI industry when facing inevitable lawsuits from authors and publishers.

  24. 24
    Article
    Avatar of bytebytegoByteByteGo·43w

    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.

  25. 25
    Article
    Avatar of sebastianraschkaSebastian Raschka·43w

    Understanding and Coding the KV Cache in LLMs from Scratch

    KV cache is a critical optimization technique for LLM inference that stores previously computed key and value vectors to avoid redundant calculations during text generation. The technique provides significant speed improvements (up to 5x in examples) by caching intermediate attention computations and reusing them for subsequent tokens. Implementation involves modifying the attention mechanism to store and retrieve cached values, though it increases memory usage and code complexity. The article provides a complete from-scratch implementation with performance comparisons and optimization strategies for production use.

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