New release from @PacktDataML at http://
amzn.to/4sjCbni "Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection" [2nd Edition; 812 pages]
RESEARCH
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Time Series Analysis with Python Cookbook Second Edition
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Researchers Introduce Meta-Harness for LLM Application Optimization
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/5 Researchers from Stanford and MIT introduce Meta-Harness: An end-to-end optimization of model harnesses Meta-Harness is a system that automatically searches for better harness code for LLM applications. They argue that LLM performance depends not only on model weights, but
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Anthropic Research Identifies Emotional Activation Patterns in Claude Sonnet 4.5
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/4 Anthropic reveals Claude uses “desperate” vectors to influence decisions Anthropic looks inside Claude Sonnet 4.5 and finds something unexpected: the model stores patterns that act like emotions. These are not feelings, but measurable activation patterns inside the network.
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Google Research on AI Agent Traps and Alibaba’s Qwen3.6-Plus Release
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/3 Google shows how the web itself can hijack AI agents with Agent Traps Alibaba launches Qwen3.6-Plus, a hosted model built for agent workflows instead of single http://
prompts.It targets agent workflows by combining reasoning, memory, and tool execution in one system. -
Stanford researchers evaluate visual reasoning in multimodal models
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/2 Stanford shows Claude and GPT generate visual reasoning without image input Stanford researchers release MIRAGE, a method to test how multimodal models use images. They take GPT-5.1, Gemini-3-Pro, Claude Opus 4.5, and Gemini-2.5-Pro and remove every image from six benchmarks.
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Princeton Study Analyzes Energy and Scaling Costs of AI Reasoning Models
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/1 Princeton study shows reasoning models use 79× more energy per query Princeton University researchers analyze how modern AI systems scale in energy, cost, and reasoning. They estimate a next-gen model training run uses 11 billion kWh, while a single reasoning query uses 33 Wh
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Deep Learning TensorFlow Keras Build Deploy ML Models
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Deep Learning with TensorFlow and Keras — Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models (3rd Ed., 667 pages): http://
amzn.to/3gitVEJ v/ @PacktDataML —————
#DataScientist #DataScience #AI #MachineLearning #ML -
Probability and Statistics Essentials for Data Science and Machine Learning
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Probability and Statistics Essentials for #DataScience and #MachineLearning, with 200+ examples and pictures [Kindle Edition]: http://
amzn.to/43a7tmo -
Mathematics of Machine Learning Book Review and Guide
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Get "Mathematics of Machine Learning" here: http://
amzn.to/4eN7i52 by @TivadarDanka v/ @PacktDataML —
GitHub: https://
github.com/cosmic-cortex/
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Here is my review: 𝗧𝗵𝗲 𝗦𝗲𝘁 𝗢𝗳 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗧𝗵𝗮𝘁 𝗟𝗲𝗮𝗿𝗻 𝗙𝗿𝗼𝗺 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 This -
Practical Guide to Reinforcement Learning from Human Feedback
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New release from @PacktDataML available at http://
amzn.to/3PMn1ZL A Practical Guide to Reinforcement Learning from Human Feedback (RLHF). Amazon Summary: RLHF is a powerful approach to AI alignment and human-centered machine learning. By combining reinforcement learning