Synthetic Data for #MachineLearning: http://
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[Purchase of book includes free PDF eBook copy]
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#AI #DeepLearning #DataScience #BigData #NLProc #ComputerVision #DataStrategy #DataScientists #PredictiveAnalytics #Analytics #DataInnovation
MACHINE LEARNING
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Synthetic Data for Machine Learning: Book Purchase with Free PDF
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Advanced Deep Learning with Python Guide
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Advanced #DeepLearning with #Python: http://
amzn.to/3FTiwTe via @PacktPublishing ———
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Python Data Science Solutions for Business Challenges and Analytics
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Dive into #DataScience — Use #Python To Tackle Tough Business Challenges: http://
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New LangChain Book: Building LLM Applications with Python and ChatGPT
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[New Book!] #GenerativeAI with #LangChain — Build Large Language Model (LLM) apps with #Python, ChatGPT, and other #LLMs: https://
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Pragmatic Machine Learning with Python: Deploy Models in Production
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"Pragmatic #MachineLearning with #Python: Learn How to Deploy Machine Learning Models in Production" Get it here: http://
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Quick Start Guide to Large Language Models Strategies and Best Practices
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Quick Start Guide to Large Language Models — Strategies and Best Practices for Using #LLMs: http://
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Deep Learning Applications for Physics Research and Scientific Discovery
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#DeepLearning for #Physics Research: https://
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DMC Improves Transformer Inference Speed Through Context Compression
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when contexts are long, attending to every single token in the past feels wasteful (and not at all how human brains work). feels like a natural setting for compression… DMC seems like a huge improvement in transformer inference speed — congrats to the authors!
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Python and Data Science Content Sharing Journey Complete
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That's a wrap! Every day, I share and simply content around Python, Data Science, Machine Learning & Large Language Models. Find me → @Sumanth_077 Like/RT the first tweet to help this reach more people.
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Implementing Transform and Inverse Transform Methods for PCA
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Now define the "transform" method that transforms the input data into a lower-dimensional representation using the calculated eigenvectors. Finally the "inverse_transform" method to reconstructs the reduced data back to the original space using eigenvectors and mean.