PANDAS Cookbook — Practical recipes for scientific computing, time series and exploratory data analysis using #Python: http://
amzn.to/3Y0XRY5 v/ @PacktDataML [3rd ed.]
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#DataScience #ComputationalScience #Analytics #MachineLearning #DataScientist
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What you will learn: The
COMPUTING
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PANDAS Cookbook: Practical Recipes for Scientific Computing and Data Analysis
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Hatch AI: Collaborative, all-in-one platform for infinite visual canvas and real-time teamwork.
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Meet Hatch AI: The collaborative, all-in-one AI platform. It combines: → Multiple purpose-built AIs
→ An infinite visual canvas
→ Real-time teamwork So you never start from scratch again. Try it at -
AI latency now crucial for human-like cognitive modeling and behavioral prediction.
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Behavioral prediction at human-like reaction speeds? We’re entering a phase where latency itself starts to matter in modeling cognition.
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Cybernetics to Computation: The Theoretical Shift in Computer Science
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The switch from cybernetics (control theory) to computation (automata theory) was profound. Semiotics is mostly useless wordcel stuff and did not substantially contribute to CS.
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Nine Algorithms That Changed Computing History
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Nine Algorithms That Changed the Future — The Ingenious Ideas That Drive Today's Computers: https://
amzn.to/41kXVnW -
Router needs 4-5x more parameter capacity
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u get it there needs to be probably 4-5x more ooms of params in the router too
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GPT-5: A Massive Leap Requires Proper Prompting for Peak Performance
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1/ GPT-5 is not just an upgrade. It’s a massive leap in:
• Tool calling
• Instruction-following
• Agentic workflows
• Coding intelligence But to get peak performance? You have to prompt it the right way. -
AI prompt frameworks: Avoid the costly mistake of assuming it knows your wants.
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The most expensive mistake in AI: assuming it knows what you want. AI is incredibly capable but terrible at mind-reading. Here are 4 frameworks for writing prompts to get shockingly good results:
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GPT-5 Energy Consumption: 18 Wh Per Response Analysis
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GPT-5 consommerait 18 Wh par réponse. Huit fois plus que GPT-4.
Et bien loin des 0,34 Wh annoncés par OpenAI, puisque GPT-4 atteindrait en réalité 2,12 Wh selon l’étude de l’Université de Rhode Island. Au delà du chiffre, et de sa méthode de calcul par hypothèse, c'est une -
Compounding Improvements in AI Algorithms, Hardware, and Infrastructure
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Same recipe as it has been: better algorithms and model architectures, continued focus on inference performance optimizations, improved hardware for inference, more efficient data center operations, etc. These things compound together pretty well, generally.