Machine Learning

"Let's learn more about our human biases to make less biased conclusions in the future." If you need it between your fingers, you can order the paperback on Amazon(link below):The Cognitive Biases Compendium

A practical approach to Prompt Engineering for developers. Dive into the world of Prompt Engineering agility, optimizing your prompts for dynamic LLM interactions. Learn with hands-on examples from the real world and elevate your developer experience with LLMs. Discover how the right prompts can revolutionize your interactions with LLMs.

Unleash your machine learning potential with "DataQuest: The Path to Kaggle Glory." Discover the secrets to dominating Kaggle competitions as you embark on an epic journey of data exploration and algorithmic mastery. From preprocessing to ensembling, this booklet is your ultimate guide to conquering the challenges and emerging victorious.

This book takes the reader from understanding the challenges of deep learning and numerical computations in the existing frameworks to the essentials of Google JAX, its functionalities, and how to leverage it in real-world machine learning and deep learning projects.

In today's fast-paced software development landscape, small development teams face numerous challenges. One of the most promising solutions to these challenges is the integration of AI-driven development tools, which can transform the way developers write, review, and collaborate on code.

My Machine Learning Journey

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. In other words, AI involves creating algorithms and computer programs that can perform tasks that usually require human intelligence, such as perception, decision-making, natural language understanding...

This book provides a comprehensive coverage of ML methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods. Also available at Google Play 

市面上的深度學習書要么晦澀難懂，要么是平台使用說明書，只講理論缺少實現或者只有編程缺少原理講解，都很難讓人理解深度學習的基本原理，本書採用理論講解與代碼實現的方式深入淺出地剖析了深度學習的技術原理和實現細節，教會讀者如何從零編寫一個深度學習庫。內容包含了：梯度下降法、回歸學習、前饋神經網絡、卷積神經網絡、循環神經網絡、生成網絡。

市場に出回っている深層学習の本は、あいまいで理解しにくいもの、またはプラットフォームの説明書であり、実装がなく理論だけが説明されているか、原理の説明がなくプログラミングだけが説明されているものであり、人々が深層学習の基本原理を理解することは困難です.この本では、理論的な説明とコードの実装を使用しています。この方法では、深層学習の技術原理と実装の詳細を簡単な方法で分析し、深層学習ライブラリをゼロから作成する方法を読者に教えています。コンテンツには、勾配降下法、回帰学習、フィードフォワード ニューラル ネットワーク、畳み込みニューラル ネットワーク、再帰型ニューラル ネットワーク、生成ネットワークが含まれます。

From machine learning to natural language processing, this book will guide you through the cutting-edge world of AI-powered data products. You'll learn how to harness the power of AI to create products that are smarter, faster, and more efficient than anything your competitors can dream of.

Talking about theory without implementation or only programming without principle explanations is difficult for people to understand the principles of deep learning. This book uses theoretical explanations and code implementations to explain the principles of deep learning and how to write a deep learning library from scratch.

"Statistics with Rust" is your comprehensive resource to unlock Rust's true potential in modern statistical methods.