Here's your opportunity to catch up on Manning's latest releases, MEAP updates, bestsellers and more! And don't forget: you always get the best deals at manning.com!
New MEAP! CUDA for Deep Learning
CUDA (Compute Unified Device Architecture) provides a powerful parallel programming model AI engineers can use to tap the massive processing power of NVIDIA GPUs. CUDA delivers direct control, debugging power, and acceleration at the GPU level that can't be matched by other types of optimizations.
CUDA for Deep Learning shows you how to work within the CUDA ecosystem, from your first kernel to implementing advanced LLM features like Flash Attention. You'll learn to profile with Nsight Compute,
identify bottlenecks, and understand why each optimization works. By solving problems at multiple levels of abstraction, you'll develop a deep understanding of CUDA, along with a practical mastery of kernel-building skills. Written for the latest NVIDIA hardware, the book builds a deep understanding of CUDA fundamentals that will stay relevant as chips upgrade and evolve. [Read more]
4 chapters of this new MEAP are available now with more to follow soon!
New liveProject! Vision Models for Classification and YOLO Segmentation
Build a high-impact computer vision pipeline that automatically identifies and analyzes elephants in images for wildlife monitoring. In this liveProject series, you'll train a custom CNN to classify Asian vs. African elephants, improve performance with transfer learning using modern pretrained models, and implement YOLOv8 segmentation for precise detection and localization.
Across three hands-on projects, you'll gain practical experience with deep learning workflows, model tuning, transfer learning, and object segmentation. By the time
you're done, you'll have portfolio-ready solutions you can apply to real-world computer vision challenges. [See more]
New liveProject! AI-Powered Document Assistant: From Text to Insights
Build a powerful AI document assistant that turns complex business files into instant, actionable insights. In this liveProject series, you'll use OpenAI's API and Python tools to extract key information, generate summaries, and answer natural-language questions with RAG across multiple document formats, all delivered as a polished, client-ready Colab app.
Across four hands-on projects, you'll develop skills in text analysis, semantic search, multi-document pipelines, and interactive dashboards. Each step is
practical and portfolio-ready, designed to showcase modern AI workflows while accelerating your real-world document intelligence capabilities. [See more]
New liveProject! Mastering Algorithms: From Smart Search to Stock Trading
Algorithms power effective software by providing clear, step-by-step solutions to common problems, and mastering them is essential for both real-world coding and technical interviews. In this series of liveProjects, you'll work as part of an IT team tasked with improving business efficiency and customer experience through hands-on algorithmic solutions. Each project applies core algorithm concepts to practical scenarios, helping you turn theory into working code.
Across seven projects inspired by Grokking Algorithms, you'll build tools like a smart file search system, a
recursive AI chatbot, a dynamic budget planner, a coin-change calculator, a route optimizer, a stock analysis engine, and a movie recommendation system. Together, they showcase search, sorting, dynamic programming, greedy methods, graph algorithms, and machine learning techniques. These projects are designed for learning rather than full production deployment, but they give you a strong, portfolio-ready demonstration of applied algorithm skills. [See more]
Now in print! Deep Learning with PyTorch, Second Edition
The powerful PyTorch library makes deep learning simple—without sacrificing the features you need to create efficient neural networks, LLMs, and other ML models. Pythonic by design, it's instantly familiar to users of NumPy, Scikit-learn, and other ML frameworks.
Now, PyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models! You'll discover how easy PyTorch makes it to build your entire DL pipeline, including using
the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier. [Read more]
The users have spoken! Agents, personalized responses, and other LLM-powered features are required in modern web applications. Build AI-Enhanced Web Apps presents the end-to-end architecture of AI web apps, including UI, backend infrastructure, data processing, API integration, deployment, and scaling, with examples and language perfect for professional web developers.
In it, you'll acquire the skills you need to complete two portfolio-ready projects: a voice-based interview
assistant and a RAG-powered corporate knowledge system. You'll also learn how to design workflows that balance latency, cost, and UX and implement responsible guardrails for security, quality control, hallucinations, and bias. [Read more]
Now in print! Machine Learning Platform Engineering
AI and ML systems have a lot of moving parts, from language libraries and application frameworks, to workflow and deployment infrastructure, to LLMs and other advanced models. A well-designed internal development platform (IDP) gives developers a defined set of tools and guidelines that accelerate the dev process, improving consistency, security, and developer experience.
Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you'll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain. [Read more]
Now in print! Software Design for Python Programmers
Great applications take advantage of established design principles and patterns that maximize performance, maintainability, and reliability. This book helps you master the "Pythonic" approach to architectural principles, such as encapsulation, abstraction, method variation, and more. The examples are in Python, but the techniques will apply to any object-oriented language.
Python applications. Following intuitive "before" and "after" examples of improved code, you'll learn to plan and execute Python applications effectively and avoid bugs associated with unmanaged state, poorly-formed classes, inflexible functions, and more. [Read more]
AI Engineering in Practice >> Chapter 7, "Prompt Types" >> Learn the one question you should ask about each component in any monolithic prompt in order to decompose it into maintainable, scalable components with proper separation of concerns.
>> When math won't give us an answer we sample! Take a tour of Monte Carlo methods and build an intuitive understanding of sampling-based inference—which is our best next stop when we're left with a posterior that we can describe mathematically but can't work with directly.
A Damn Fine Stable Diffusion Book >> Chapter 0, "Introduction" >> Discover the parallel between Stable Diffusion and early hiphop as you explore the basics of generative AI, the benefits of using open models and open-source tools for image generation, and what's in store for you in the chapters to come.
to gather user input for enhancing user experience, and understand the mathematical principles that provide precise control over every pixel—all contributing to that feels-right vibe!
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