Artificial Intelligence Programming with #Python — From Zero to Hero: http://
amzn.to/43TkNea [720 pages]
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#DataScience #DataScientist
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Contents: Thorough introductions to #AI, #MachineLearning, and #DeepLearning Hands-on introductions to Python Discussions of
MACHINE LEARNING
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AI Programming with Python: Complete Guide from Basics to Advanced
By
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Handbook of Data Science and AI: Generating Value with Machine Learning
By
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The Handbook of #DataScience and #AI — Generate Value from Data with #MachineLearning and Data #Analytics: http://
amzn.to/3Wc1Iiz
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#DataScientist #ML #DataStrategy #AnalyticsStrategy #DataLeadership #Datapreneurship #Datapreneur @LeadershipData #CDO #CMO #CTO #CAIO #Startups -
Machine Learning for Data Streams: Essential for Modern Digital Enterprises
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#MachineLearning for Data Streams is imperative for digital enterprises to move at the speed of data!
★★★★
Get the book: http://
amzn.to/3oGnI41
★★★★
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#IoT #IIoT #DataScience #AI #ML #StreamAnalytics #EdgeComputing #Edge #EdgeAI #AnomalyDetection #DataScientist -
Essential Books on Machine Learning Mathematics and AI Foundations
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Why Machines Learn – The Elegant Math Behind Modern AI: http://
amzn.to/4dwliQc
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Deep Learning Foundations and Concepts: http://
amzn.to/4mw2xAE
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#DataScience #AI #ML #MachineLearning #Mathematics #LinearAlgebra #Probability #Calculus #DataScientist -
How Coders Can Learn AI: A Practical Learning Path
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if you know how to code, and want to learn AI, this is what you should do: lucky for you the field has gotten *less* deep and much easier to learn about over the last few years. most lab roles require specific knowledge (model training, CUDA kernels, etc.). each takes ~18 months
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YOLOv8 Fruit Detection 4K on Orange Pi 5 Plus
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4K Fruit Detection on Orange Pi 5 Plus with Metis M.2
Three YOLOv8 models. 4K camera input. One Metis M.2.
Now running on an Orange Pi 5 Plus. If you’re working with edge devices and need efficient, high-performance AI inference at a very appealing price point—this demo -
Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning
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Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning Yoon et al.: https://
arxiv.org/abs/2506.09498 #ArtificialIntelligence #DeepLearning #MachineLearning -
Small Reasoning Models Training Research Artifact Released
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Thanks to my excellent colleagues @timseyde and @rohin_manvi
, who did most of this work! We hope this research artifact will be useful as a recipe for training small reasoning models. More to come very soon! 🙂 -
SFT and GRPO Improve LFM-1B Math Reasoning Performance
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The most interesting part is that the base LFM-1B model isn't that strong in math (see results). Extensive SFT (~100B tokens) was enough to turn it into a strong reasoner. Further GRPO compressed the reasoning traces and even maintained performance.
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Fine-tuned Model Response Length Analysis Across AI Models
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You can see the mean response length for correct and incorrect answers across different models. The source model is on the left and the fine-tuned model on the right. Average performance of the fine-tuned model is reported at the top as "32k score | 4k score".