Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
This book is a extensive guide to machine learning and deep learning using PyTorch’s straightforward framework. It serves as both a step-by-step tutorial and a lasting reference as you enhance your machine learning systems. With clear explanations,visualizations,and practical examples,you’ll dive deep into key techniques and principles that empower you to build your own models and applications. Covering everything from scikit-learn for machine learning to advanced topics like generative adversarial networks (GANs) and large-scale transformers for NLP, this book is essential for developers and data scientists eager to create practical applications. A good understanding of Python, calculus, and linear algebra will set you on your path to exploring this exciting field.
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Machine Learning System design Interview
s are regarded as some of the toughest technical interview questions. This book offers a dependable strategy and a rich knowledge base for tackling a wide variety of ML system design queries. It presents a step-by-step framework to approach ML system design questions, replete with real-world examples that demonstrate a systematic method supported by detailed, actionable steps. It is indeed an invaluable resource for both beginners and seasoned engineers interested in ML system design. If you’re preparing for an ML interview, this book is tailored for you. Inside you’ll find:
- An insider perspective on what interviewers seek and why.
- A comprehensive 7-step framework to solve any ML system design interview question.
- 10 authentic ML system design interview questions accompanied by thorough solutions.
- 211 diagrams that visually clarify how various systems operate.
Table of Contents:
- Chapter 1: Introduction and Overview
- Chapter 2: Visual Search System
- chapter 3: Google Street view Blurring System
- Chapter 4: YouTube Video Search
- Chapter 5: Harmful Content detection
- Chapter 6: Video Recommendation System
- Chapter 7: Event Recommendation System
- Chapter 8: Ad Click Prediction on Social Platforms
- Chapter 9: Similar Listings on Vacation Rental Platforms
- Chapter 10: Personalized News Feed
- Chapter 11: People you May Know
Publisher: ByeByteGo | Publication date: January 28, 2023 | Language: English | Print length: 294 pages | ISBN-10: 1736049127 | ISBN-13: 978-1736049129 | Item Weight: 1.08 pounds | Dimensions: 7 x 0.67 x 10 inches | Best Sellers Rank: #8,246 in Books | Customer Reviews: 4.5 out of 5 stars (256 ratings)
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and Techniques to Build Intelligent Systems
Through a recent series of breakthroughs,deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you’ve learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end, explore several models including support vector machines, decision trees, random forests, and ensemble methods, and exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection. Dive into neural net architectures including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers. Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning.
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The Hundred-Page Machine Learning Book (The Hundred-Page Books)
The hundred-Page Machine Learning Book
Master machine learning through clarity, not complexity―in a book engineered to teach with exceptional conciseness. Translated into 11 languages and used in thousands of universities worldwide, this book takes a unique approach: it assumes that your time is valuable. Instead of drowning you in theory or skimming the surface,it delivers a complete education in modern machine learning,focusing on what matters in practice. Covering essential mathematical concepts and practical skills like feature engineering and model evaluation, it provides a complete toolkit for solving modern machine learning challenges.Each chapter reflects the author’s real-world experience,focusing on techniques that work in practice and ensuring readers gain crucial understanding without getting overwhelmed by complex equations.
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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
Machine learning systems are both complex and unique.They consist of various components and involve multiple stakeholders, while being heavily dependent on varying data use cases. This book provides a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptable. Author chip Huyen discusses critical design decisions-like data processing, feature selection, model retraining frequency, and monitoring-in a manner that aligns with overall system goals. Through it’s iterative framework and real-world case studies, you’ll learn to tackle challenges such as:
- Engineering data and selecting the right metrics to solve business problems
- Automating model development, evaluation, deployment, and updates
- Creating a monitoring system for fast issue detection in production
- Architecting versatile ML platforms across various use cases
- Developing responsible ML systems
This book is published by O’Reilly Media and provides essential skills and understanding for innovating in the world of machine learning. With a length of 386 pages, it ranks highly in Business Intelligence Tools and Machine Theory. It’s an invaluable resource with a 4.6 out of 5-star customer rating.
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don’t miss the chance to elevate your understanding of machine learning systems!
why Machines Learn: The Elegant Math Behind Modern AI
Anil Ananthaswamy’s captivating book dives deep into the mathematics that powers machine learning and sparks the current AI revolution. From approving loan applications to assessing tumor risks, AI systems are reshaping critical decision-making in various fields, including biology and physics. Grounded in the timeless principles of linear algebra and calculus,these advancements arose from the intersection of computer science and gaming technology in the 1990s. Ananthaswamy explores the profound connections between artificial and natural intelligence, emphasizing the importance of understanding the math behind these technologies to navigate their strengths and limitations.
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