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! Performance Engineering in Practice
You can prevent most software performance problems if you recognize their predictable patterns. Performance Engineering in Practice introduces author Den Odell's Fast by Default model that replaces the slow fix-it-later path with a unique framework to build in performance from the start.
Den Odell's System Paths framework provides a universal diagnostic language for performance that works across any platform or stack to keep your systems fast even as your codebase, team, and user
base grow. You'll work through hands-on examples like slow internal dashboards and cascading problems from a degraded API. Effective profiling tools, shared dashboards, CI budgets, and design reviews all ensure your performance goals are user-centered and your team is aligned and accountable. [Read more]
5 chapters of this new MEAP are available now with more to follow soon!
We've teamed up with Neo4J to give away this free eBook
If you don't ground your LLM, there's a high likelihood that your GenAI app will hallucinate. Without the right context, your AI will likely provide wrong or even nonsensical results.
There's a fix: the tag team of RAG and knowledge graphs. If you connect your data using a knowledge graph, you supply the context your LLM needs to give more nuanced, accurate responses. You can even combine structured and unstructured data.
We've teamed up with Neo4J to give away copies of Essential GraphRAG, a comprehensive guide on how to build a GraphRAG system from scratch. You'll learn basic RAG concepts, advanced techniques,
and how to evaluate accuracy for your GenAI application.
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]
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]
The RLHF Book >> Appendix B, "Beyond 'Just Style'" & Appendix C, "Practical Issues" >> Discover the two reasons that style is core to understanding the value that RLHF provides (as well as being a never-ending source of human value). Then take a look at a list of lessons that cover practical considerations for running post-training experiments at scale.
decision-making—his solution to complex problems that previously were tackled only with "an educated guess and a prayer." Then delve into the transformative framework that makes it possible to ask questions that once seemed qualitative in quantitative terms, and learn about Vladimir Vapnik's answer to the problem of overfitting (and what geometric intuition brings to the equation) as you examine an algorithm that redefines classification.
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.
You're receiving this email because you're subscribed to our newsletters. To manage what emails you get, visit our preference center. You may unsubscribe from this promotional mailing list at any time. You will still receive updates on your MEAPs and other Manning books.
Manning Publications, 20 Baldwin Road, PO Box 761, Shelter Island, Suffolk, New York, 11964, US
No comments:
Post a Comment