Showing results for "Machine Learning Design Patterns" in All Categories

Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. 

A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumour is cancerous, or deciding whether...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. 

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning―the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point...

Unlock the Secrets of AI Deep Learning and Neural Networks: Empower Your Future in Minutes a Day! Ready to dive into the world of artificial intelligence, but don’t know where to start? Whether you're a student, developer, or curious learner, this book takes you on a journey to master deep learning concepts, demystify neural networks, and transform your skills into real-world impact—all at your own pace.

The deep-learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep-learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

A collection of shockingly prescient stories covering topics from aliens to AI, including some that have never before been seen, from the New York Times bestselling author of the Silo trilogy Hugh Howey is known for crafting riveting and immersive page-turners of boundless imagination, spawning...

As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way.

Revolutionize Your Trading Strategies with AI and Machine Learning! In today's fast-paced financial world, Artificial Intelligence (AI) and Machine Learning (ML) are transforming how trading and investment decisions are made. Advanced algorithms and powerful computing capabilities allow traders to analyze vast datasets, identify patterns, and execute trades with unmatched speed and precision. AI and Machine Learning in Trading: Harnessing Technology for Smarter Strategies, authored by Dr. Paul C. Ajulufoh, is your essential guide to leveraging AI and ML in the financial markets. This ...

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and...

In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.

If you need a go-to guide with numerous examples and coded programs, Python Machine Learning will be the answer. If you want to train your own models and enhance them to achieve your demand, Python Machine Learning is the exact tool. Do you want to train the machines and model to carry out complex jobs with accurate date and predictions in a more efficient way? Do you want to have a quick start in the right direction to enter into the Python world?  

Most production ML failures are data engineering failures: a feature pipeline that drifted overnight, a schema that changed without notice and corrupted a backfill, an ingestion job that silently dropped records during peak inference. For the architects and engineers who own those systems, the cost is measured in degraded models, missed SLOs, and postmortems that circle back to the same root cause: the data infrastructure was not designed to hold production conditions. Inside this book, readers will learn how to: Design durable schemas: Build feature schemas with explicit nullability, ...

What if you could turn raw data into powerful, responsible intelligence without a PhD in computer science? Building a Comprehensive AI Model is your roadmap to designing, training, fine-tuning, and deploying cutting-edge AI systems. From your very first classifier to state-of-the-art Large Language Models and Generative AI with a peek at DeepSeek. Marc Stanford demystifies the entire workflow, enabling you to create technology that ethically transforms industries. Inside, you'll master: • Collecting & cleaning data like a pro; no more 'garbage in, garbage out.' • Choosing the right ...

Most AI Engineers today don't train models. They build systems around them. By 2023, the dominant pattern had flipped: instead of training custom classifiers, most teams call a foundation model like Claude, GPT, or Gemini and get in one request what used to take a data science team months. That shift didn't eliminate engineering work — it relocated it. The hard problems moved from "how do we train a model that generalizes" to "how do we design, orchestrate, evaluate, secure, and operate a system built on a model we don't control and can't fully inspect." AI Engineering Foundations is a ...

In this highly intoxicating original series, researchers Brian Christian and Tom Griffiths, authors of the best-selling smash hit, Algorithms to Live By, tackle some of the biggest ideas in computer science today - and, in the process, illuminate cutting-edge ways of understanding how we live, work and play.

Ned discovers a hard truth about his past but will it also endanger the future of the universe? After reconnecting with an old acquaintance, Hank’s AI companion is on the hunt for more information and it just might bring them closer than ever to Twain. But the old Enemy AI has a few tricks up his sleeve. And while they pursue the villain, the crew also find themselves in the crosshairs. Having angered a shadowy sect, the hunters become the hunted.

In this audiobook, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general listener, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.

A primer on Machine Learning tailored for individuals interested in exploring the field before advancing to more complex literature by the author, including 'Data Science' and other works. ('Data Science' achieved the #1 spot in Mathematical Analysis on Amazon).

The Architecture of Patterns Design Patterns and Architectural Judgment in Long-Lived Software Systems Design patterns are often presented as reusable solutions—clean structures waiting to be applied. In real systems, they are something else entirely. They are responses to pressure. Over time, software systems accumulate forces: changing requirements, growing teams, expanding domains, and the limits of human comprehension. Patterns emerge not because they are elegant in theory, but because certain structures survive sustained use better than others. The Architecture of Patterns examines ...