Weekly pill-sized articles on key Machine Learning concepts. 🤖 Subscribe to our newsletter 👇

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For anyone ready to master RL, this book seems to cover it all—from foundational principles to the latest methods used in real-world industry! It will be released by the end of this week! 👇 packtpub.com/en-us/product/d…
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I've just started reading the early review copy of "Deep Reinforcement Learning Hands-On" by Maxim Lapan, a practical guide packed with advanced applications! Here’s what makes it a standout read 🧵 👇
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SQL Joins clearly explained 👇🏻
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Be careful with cross validation 👇
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The steps of any Data Science project 👇
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How Python work in our computer clearly explained👇🏻
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EDA clearly explained 👇
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How to make your LLMs smarter and more efficient explained!👇🏻 (Don't forget to bookmark for later 😉)
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Multi Query, an Advanced Retrieval Strategy for RAG, clearly explained 👇
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K-Nearest Neighbors clearly explained 👇
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Logistic Regression clearly explained 👇
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There are several types of data distributions you might encounter in a dataset. Here are some common ones 👇🧵
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🚨NEVER split your data randomly! At least when working with Time Series data... Learn here what are the dangers of doing so 🧵 👇
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Retrieval refers to the process of finding and extracting relevant information or documents from a large collection or database in response to a query or information need. Discover more 🧵 👇
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Let's discuss another retrieval method: "Maximum Marginal Relevance" or MMR. 👉 MMR balances relevance and diversity in search results. Let's learn more about it 🧵 👇
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📚 I've just read "Transformers for Natural Language Processing and Computer Vision" by Denis Rothman. A must read that offers deep insights into transformer architectures and their practical applications in NLP and computer vision. 🧵👇
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