Название: Optimizing AI and Machine Learning Solutions: Your ultimate guide to building high-impact ML/AI solutions
Автор: Mirza Rahim Baig
Издательство: BPB Publications
Год: 2024
Страниц: 836
Язык: английский
Формат: pdf, epub
Размер: 26.0 MB

Build high-impact ML/AI solutions by optimizing each step.

Key Features:
- Build and fine-tune models for maximum performance.
- Practical tips to make your own state-of-the-art AI/ML models.
- ML/AI problem solving tips with multiple case studies to tackle real-world challenges.

Description:
This book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance. It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for Deep Learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques. Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting. They will also benefit from Deep Learning by optimizing models for image processing, natural language processing (NLP), and specialized applications. The reader can put theory into practice with hands-on case studies and code examples, reinforcing their understanding. With this book, the reader will be able to create high-impact, high-value ML/AI solutions by optimizing each step of the solution building process, which is the ultimate goal of every Data Science professional.

With this book, you will be able to approach a ML/AI solution in a systematic way, optimizing each step of the solution building process. You will thus be able to create high impact, high value Machine Learning/Artificial Intelligence solutions – which is the ultimate goal of every Data Science professional.

Chapter 1: Optimizing a Machine Learning /Artificial Intelligence Solution – introduces some core concepts and sets up the foundation for our journey together in this book. It provides an overview of machine learning, followed by addressing the various practical challenges in machine learning. It introduces some key ideas which will be expanded on in the later chapters. It is crucial to distinguish between simply making a model and carefully designing an end-to-end solution to the business problem. A framework to approach such end-to-end solutions is introduced and the chapter will point to the chapters in the book that help you optimize the solution at each step highlighted in the framework, to ultimately develop a truly optimized machine learning/artificial intelligence solution.

Chapter 2: ML Problem Formulation: Setting the Right Objective – discusses perhaps the most important step in any data science project that involves machine learning problem formulation. A problem can be formulated in various ways employing different solutions: machine learning or not. Further, there are multiple possible ML approaches. Making these decisions is not a trivial matter. The chapter also highlights the important of aligning the model objective with the business objective for achieving maximum impact.
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Chapter 10: Optimizing Natural Language Processin Models – continues the deep learning deep dive by focusing on NLP models. The chapter introduces the peculiarities of natural language data and teaches handling them by appropriate text pre-processing and data representation (using embeddings). The chapter details the recurrent and architecture and the transformer architecture with hands on case studies. In addition to hyper-parameter tuning for NLP models, the chapter shares tricks like using 1D convolutions and pre-trained embeddings (a prelude to transfer learning).

Chapter 11: Transfer Learning – teaches you how to stand on the shoulders of giants and benefit from the excellent work done by the ML/AI community. The chapter explains the different types of transfer learning and how they are helpful in modern machine learning/ artificial intelligence, highlighting both the benefits and limitations of it. The chapter demonstrates the usage of a SOTA model out of the box and eventually how to fine tune it for better performance. Transfer learning is slightly different for image processing and NLP tasks. The chapter will employ multiple SOTA models for both image and NLP tasks, employing popular libraries.

What you will learn:
- End-to-end solutions to ML/AI problems.
- Data augmentation and transfer learning.
- Optimizing AI/ML solutions at each step of development.
- Multiple hands-on real case studies.
- Choose between various ML/AI models.

Who this book is for:
This book empowers data scientists, developers, and AI enthusiasts at all levels to unlock the full potential of their ML solutions. This guide equips you to become a confident AI optimization expert.








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