Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Julian and David will cover the Hackathon project they worked on that won at the New York Stock Exchange— fine tuning an LLM to generate summaries for airflow task failures.

Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.

Pearson’s Artificial Intelligence encompasses a comprehensive text on the fundamental principles and concepts of Artificial Intelligence—a new-age technology that fuels the much-coveted ‘Industry 4.0’. Presented in lucid English, this book covers all the basic concepts, enriched with latest examples. It also discusses all the major components of AI, such as Neural Networks, Natural Language Processing, Reinforcement Learning, Machine Learning, Deep Learning and Computer Vision. The book is a deliberation of classical as well modern AI techniques and related technologies that provides readers with an overall knowledge and understanding of AI in present-day context.

If you're ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about. Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises. WHAT'S INSIDE: FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts. WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing. PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more. You'll also explore how to: Manage the various sources of randomness in neural network training Differentiate between encoder and decoder architectures in large language models Reduce overfitting through data and model modifications Construct confidence intervals for classifiers and optimize models with limited labeled data Choose between different multi-GPU training paradigms and different types of generative AI models Understand performance metrics for natural language processing Make sense of the inductive biases in vision transformers If you've been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.

This session demonstrates how to use large language models (LLMs) to translate ideas directly into cloud architecture blueprints. You’ll learn how to generate designs from these blueprints with natural language processing. We’ll also use an existing LLM model specialized in code generation to understand our language dialect to generate cloud architecture diagrams. Finally, we'll also show you a web app on Google Cloud that allows users to interact with the model and use the generated artifacts in practice.

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Join us to learn how DaVita leverages DocAI and Healthcare NLP to transform kidney care. This session highlights the power of AI in analyzing medical records, uncovering critical patient insights, and reducing errors. Discover how AI enables physicians to focus on personalized care, resulting in significant improvements in healthcare delivery. Join us for this Mini Talk at 'Meet the Experts, hosted by Google Cloud Consulting' at Expo. Seating is limited and on a first-come, first served basis; standing areas are available.

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Join us for an insightful session that explores the exciting future of Google Cloud‘s managed databases, including Cloud SQL, AlloyDB, and Spanner. Vector search capabilities deeply integrated into operational databases enable powerful enterprise generative AI apps. Additionally, learn how AI has the potential to revolutionize the way applications interact with databases. We will delve into exciting frontiers: Natural language processing in databases, and app migration with large language model-powered code migration.

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The latest Google Compute Engine instance families have built-in AI hardware accelerators delivering GPU-level performance for computer vision, natural language processing, speech recognition, recommendation engines, and generative AI models all powered by the latest Intel 4th and 5th gen Xeon CPUs. We will demo a real-time chatbot and CodeGen generative AI application, share performance results across various AI workloads, and highlight our partnerships with the AI software ecosystem and Google Cloud end users.

By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Explore Mongodb Atlas — MongoDB’s developer data platform, and learn how to integrate it with various Google Cloud services. During this lab lounge, you will create a fully managed database deployment, set up serverless Triggers that react to database events, and build Atlas Functions to communicate with Google Cloud APIs.

Additionally, you will explore Google Cloud’s NLP APIs, perform sentiment analysis on incoming data, learn how to replicate operational datasets from MongoDB Atlas to BigQuery and build an ML model for classification.

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Large language models offer capabilities that overlap with more traditional approaches to natural language processing tasks like translation. Multimodal large language models have an even broader overlap with traditional speech and image models. You can now choose which approach best suits your needs. Here, you will learn about their strengths and weaknesses compared to neural machine translation techniques and receive an overview of the latest advancements in our SOTA Cloud Translation API, combing ease of use with contextual capabilities of generative AI models, to enhance our customers' translations at scale. Experience how new models trained to perform both transcription and translation can go from speech to text in a target language using one large model.

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Practical methods for analyzing your data with graphs, revealing hidden connections and new insights. Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. About the Technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the Book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's Inside Creating knowledge graphs Node classification and link prediction workflows NLP techniques for graph construction About the Reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the Author Tomaž Bratanič works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Quotes Undoubtedly the quickest route to grasping the practical applications of graph algorithms. Enjoyable and informative, with real-world business context and practical problem-solving. - Roger Yu, Feedzai Brilliantly eases you into graph-based applications. - Sumit Pal, Independent Consultant I highly recommend this book to anyone involved in analyzing large network databases. - Ivan Herreros, talentsconnect Insightful and comprehensive. The author’s expertise is evident. Be prepared for a rewarding journey. - Michal Štefaňák, Volke

Snowflake has been foundational in the data space for years. In the mid-2010s, the platform was a major driver of moving data to the cloud. More recently, it's become apparent that combining data and AI in the cloud is key to accelerating innovation. Snowflake has been rapidly adding AI features to provide value to the modern data stack, but what’s really been going on under the hood? At the time of recording, Sridhar Ramaswamy was the SVP of AI at Snowflake, being appointed CEO at Snowflake in February 2024. Sridhar was formerly Co-Founder of Neeva, acquired in 2023 by Snowflake. Before founding Neeva, Ramaswamy oversaw Google's advertising products, including search, display, video advertising, analytics, shopping, payments, and travel. He joined Google in 2003 and was part of the growth of AdWords and Google's overall advertising business. He spent more than 15 years at Google, where he started as a software engineer and rose to SVP of Ads & Commerce.  In the episode, Richie and Sridhar explore Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, how NLP and AI have impacted enterprise business operations as well as new applications of AI in an enterprise environment, the challenges of enterprise search, the importance of data quality, management and the role of semantic layers in the effective use of AI, a look into Snowflakes products including Snowpilot and Cortex, the collaboration required for successful data and AI projects, advice for organizations looking to improve their data management and much more.     About the AI and the Modern Data Stack DataFramed Series This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratizationAdding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data managementAccelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI Links Mentioned in the Show: SnowflakeSnowflake acquires Neeva to accelerate search in the Data Cloud through generative AIUse AI in Seconds with Snowflake Cortex[Course] Introduction to SnowflakeRelated Episode: Why AI will Change Everything—with Former Snowflake CEO, Bob MugliaSign up to a...

Principles of Data Science offers an end-to-end introduction to data science fundamentals, blending key mathematical concepts with practical programming. You'll learn how to clean and prepare data, construct predictive models, and leverage modern tools like pre-trained models for NLP and computer vision. By integrating theory and practice, this book sets the foundation for impactful data-driven decision-making. What this Book will help me do Develop a solid understanding of foundational statistics and machine learning. Learn how to clean, transform, and visualize data for impactful analysis. Explore transfer learning and pre-trained models for advanced AI tasks. Understand ethical implications, biases, and governance in AI and ML. Gain the knowledge to implement complete data pipelines effectively. Author(s) Sinan Ozdemir is an experienced data scientist, educator, and author with a deep passion for making complex topics accessible. With a background in computer science and applied statistics, Sinan has taught data science at leading institutions and authored multiple books on the topic. His practical approach to teaching combines real-world examples with insightful explanations, ensuring learners gain both competence and confidence. Who is it for? This book is ideal for beginners in data science who want to gain a comprehensive understanding of the field. If you have a background in programming or mathematics and are eager to combine these skills to analyze and extract insights from data, this book will guide you. Individuals working with machine learning or AI who need to solidify their foundational knowledge will find it invaluable. Some familiarity with Python is recommended to follow along seamlessly.

"MATLAB for Machine Learning" is your comprehensive guide to leveraging MATLAB's powerful tools and toolbox for machine learning and deep learning tasks. Through this book, you will explore practical applications and processes that streamline the development of machine learning models while tackling real-world problems effectively. What this Book will help me do Gain proficiency in utilizing MATLAB's Machine Learning Toolbox for developing machine learning algorithms. Learn how to handle data preprocessing, from data cleansing to visualization, within MATLAB. Explore and implement foundational to advanced machine learning techniques, such as classification and regression models. Comprehend and apply the principles of neural networks for pattern recognition and cluster analysis. Dive into advanced concepts of deep learning, including convolutional networks, natural language processing, and time series analysis, using MATLAB's inbuilt functionality. Author(s) Giuseppe Ciaburro is an expert in the field of machine learning and MATLAB programming. With a robust academic background in data science and years of experience in applying these principles across domains, Giuseppe provides a clear and approachable pathway for learners in his writing. Who is it for? This book is ideal for machine learning professionals, data scientists, and engineers specializing in fields such as deep learning, computer vision, and natural language processing. It is suitable for those with a fundamental understanding of programming concepts who seek to apply MATLAB in solving complex learning problems. A prior familiarity with MATLAB basics will be advantageous.

We talked about:

Atita’s background How NLP relates to search Atita’s experience with Lucidworks and OpenSource Connections Atita’s experience with Qdrant and vector databases Utilizing vector search Major changes to search Atita has noticed throughout her career RAG (Retrieval-Augmented Generation) Building a chatbot out of transcripts with LLMs Ingesting the data and evaluating the results Keeping humans in the loop Application of vector databases for machine learning Collaborative filtering Atita’s resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/atitaarora/
Twitter: https://x.com/atitaarora Github: https://github.com/atarora Human-in-the-Loop Machine Learning: https://www.manning.com/books/human-in-the-loop-machine-learning Relevant Search: https://www.manning.com/books/relevant-search Let's learn about Vectors: https://hub.superlinked.com/ Langchain: https://python.langchain.com/docs/get_started/introduction Qdrant blog: https://blog.qdrant.tech/ OpenSource Connections Blog: https://opensourceconnections.com/blog/

Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

In this talk we will have a look at Haystack, an open source LLM framework, and how we can use it to create custom, private search systems on our own data. We will look at how we can build retrieval augmented generative pipelines for our Notion pages, and how Haystack can help you create custom tooling for larger NLP applications.