Make Codex for web a whole lot more powerful, it's a more convenient form factor for off-peak usage since I can control it from my phone
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Python Machine Learning By Example Third Edition Book Release
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Python Machine Learning By Example: http://
amzn.to/3MczBwk v/ @PacktDataML +
GitHub: https://
github.com/PacktPublishin
g/Python-Machine-Learning-By-Example-Third-Edition
… 518-pages! What you will learn: Machine learning best practices throughout data preparation and model development Build and improve image classifiers using -
Build AI-Enhanced Web Apps with LLMs
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Build AI-Enhanced Web Apps: http://
amzn.to/4bgux6d
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Amazon Summary: "This book shows you step-by-step and example-by-example how to build sites and applications that take advantage of large language models (LLMs) like GPT, Claude, and Llama. Written especially for web -
Stanford’s Free AI Course: Transformers and Large Language Models
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If you’re serious about AI, this is worth your attention. Stanford has just released its course CME 295: Transformers & Large Language Models in full on YouTube. What stands out to me is the level of clarity and structure. This isn’t another surface-level overview. It’s the actual curriculum used to teach how modern AI systems work. This will help you move from using AI to understanding it. 📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & MoEs • LLM finetuning (LoRA, RLHF, supervised) • Evaluation techniques (LLM-as-a-judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling • Agentic workflows (RAG, tool calling) 🎥 Watch these now: – Lecture 1: zurl.co/F0QR5 – Lecture 2: zurl.co/hG5lp – Lecture 3: zurl.co/PnKrW – Lecture 4: zurl.co/XCZoE – Lecture 5: zurl.co/GWlYI – Lecture 6: zurl.co/zGqqQ – Lecture 7: zurl.co/T06NM – Lecture 8: zurl.co/Un42q – Lecture 9: zurl.co/rR3YL For 2026, consider setting aside 2–3 hours each week to go through these lectures. If you’re working in AI whether on infrastructure, agents, or applications, this is a foundational resource worth your time. It’s a simple way to build depth where it matters most. #AI #LLMs #Transformers #Stanford #GenAI
→ View original post on X — @pascal_bornet, 2026-04-03 05:00 UTC
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50 Essential Algorithms for Every Programmer to Learn
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50 Algorithms Every Programmer Should Know: http://
amzn.to/3Mi1hAk v/ @PacktDataML #DataScientist #DataScience #Mathematics #MachineLearning #ML #ComputerScience #ComputationalScience -
30 Essential AI Agents for Production-Ready Systems
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30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architectures and patterns: https://
amzn.to/41ckg6z via @PacktDataML —
What you will learn:
Deploy production-ready agent systems that scale securely and reliably
Use LangChain and -
Python for Data Science: Hands-On Introduction Book
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Python for Data Science — A Hands-On Introduction: http://
amzn.to/44LcjEA
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#DataScience #AI #ML #MachineLearning #DataScientist -
Bayesian Data Analysis and Probabilistic Modeling in Python
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Bayesian Data Analysis [download 677-page PDF Statistics eBook] at http://
sites.stat.columbia.edu/gelman/book/
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Do it in Pythonusing this Practical Guide to Probabilistic Modeling: http://
amzn.to/3w3tq9g —————
#DataScience #DataScientist #MachineLearning #ML #Inference #StatisticalLearning -
50 Machine Learning Projects to Understand Large Language Models
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50 ML projects to understand LLMs — Investigate transformer mechanisms through data analysis, visualization, and experimentation: http://
amzn.to/4aPfP7q
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#AI #GenAI #MachineLearning #DataScientist #DataScience -
Mastering PyTorch: Create and Deploy Deep Learning Models
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"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" – http://amzn.to/40IFEQR via @PacktDataML —————
#AI #ML #MachineLearning #DataScience #DataScientist #GenAI