#AI Engines: Book Review. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/AI-Engine
MACHINE LEARNING
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AI Engines Book Review: Technologies and Frameworks Overview
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Neural Networks in SQL Server: Machine Learning Implementation Guide
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Intro to Neural Networks in #SQLServer! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #MSSQLServer #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/NNs-SQLServer -
Essential Crash Course Resources for Data Science and AI Beginners
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Crash Course for Newbies! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Computer-Crash
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Python for TensorFlow Pocket Primer Guide
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Python for TensorFlow Pocket Primer! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Python-TF -
Master Basic SQL Commands for Data Science Analytics
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Basic #SQL Commands! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/SQL-Commands -
Training Large Models Has Become Trivially Easy in 2023
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Sir, this is 2023. Training over a hundred million parameters is what happens when you press the "train Lora" button on the HTML interface.
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LLM Token Limits Prevent True Reasoning Capability
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It's not an assumption. Reasoning, as I define it, is simply not doable by a system that produces a finite number of tokens, each of which is produced by a neural net with a fixed number of layers.
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LLM reasoning vs rationalization and code as inner monologue
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This used to always be true — If you ask an LLM "Why did you say that?" the answer is rationalization / guessing. It's only possibly reasoning if it comes before the answer. Code use is the asterisk: Interpreted code blocks serve as a de facto "inner monologue" it can recall.
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Automatic Differentiation Explained in 30 Lines Python
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Victor MARTIN ~ Understanding Automatic Differentiation in 30 lines of Python https://
bit.ly/3t0ejMg #AI #MachineLearning #DeepLearning #LLMs #DataScience -
LLM Buffer Limitations: Non-Persistent Memory Architecture Challenges
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That doesn't work for anything with non-persistent buffers (which is most current LLMs AFAICT, since the sin/cos cache is in those buffers)