Resultados de Pesquisa

INGLÊS

Just Enough Data Science And Machine Learning - Essential Tools And Techniques

de Mark Levene e Martyn Harris

~~52,99€~~ 47,69€ 10% IMEDIATO

DISPONIBILIDADE IMEDIATA

 An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: <ul> <li>Clear explanations of fundamental statistical notions and concepts</li> <li>Coverage of various types of data and techniques for analysis</li> <li>In-depth exploration of popular machine learning tools and methods</li> <li>Insight into specific data science topics, such as social networks and sentiment analysis</li> <li>Practical examples and case studies for real-world application</li> <li>Recommended further reading for deeper exploration of specific topics.</li> </ul>
INGLÊS

Quick Start Guide To Large Language Models - Strategies And Best Practices For Chatgpt, Embeddings, Fine-Tuning, And Multimodal Ai

de Sinan Ozdemir

~~52,99€~~ 47,69€ 10% IMEDIATO

DISPONIBILIDADE IMEDIATA

 The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs'' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). <ul> <li>Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more</li> <li>Use APIs and Python to fine-tune and customize LLMs for your requirements</li> <li>Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents</li> <li>Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting</li> <li>Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI</li> <li>Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets</li> <li>Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5</li> <li>Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind</li> <li>Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks</li> </ul> "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Quick Start Guide To Large Language Models - Strategies And Best Practices For Chatgpt, Embeddings, Fine-Tuning, And Multimodal Ai

de Sinan Ozdemir

~~42,39€~~ 38,15€ 10% IMEDIATO

DISPONIBILIDADE IMEDIATA

 The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs'' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). <ul> <li>Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more</li> <li>Use APIs and Python to fine-tune and customize LLMs for your requirements</li> <li>Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents</li> <li>Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting</li> <li>Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI</li> <li>Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets</li> <li>Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5</li> <li>Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind</li> <li>Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks</li> </ul> "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Quick Start Guide To Large Language Models - Strategies And Best Practices For Using Chatgpt And Other Llms

de Sinan Ozdemir

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs'' inner workings to help you optimize model choice, data formats, parameters, and performance. You''ll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). <ul> <li>Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more</li> <li>Use APIs and Python to fine-tune and customize LLMs for your requirements</li> <li>Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation</li> <li>Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting</li> <li>Customize LLM embeddings to build a complete recommendation engine from scratch with user data</li> <li>Construct and fine-tune multimodal Transformer architectures using opensource LLMs</li> <li>Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)</li> <li>Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind</li> </ul> "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application." --Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Visual Analytics Fundamentals - Creating Compelling Data Narratives With Tableau

de Lindy Ryan

47,69€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Master the Fundamentals of Modern Visual Analytics--and Craft Compelling Visual Narratives in Tableau! Do you need to persuade or inform people? Do you have data? Then you need to master visual analytics and visual storytelling. Today, the #1 tool for telling visual stories with data is Tableau, and demand for Tableau skills is soaring. In Visual Analytics Fundamentals, renowned visual storyteller and analytics professor Lindy Ryan introduces all the fundamental visual analytics knowledge, cognitive and perceptual concepts, and hands-on Tableau techniques you''ll need. Ryan puts core analytics and visual concepts upfront, so you''ll always know exactly what you''re trying to accomplish and can apply this knowledge with any tool. Building on this foundation, she presents classroom-proven guided exercises for translating ideas into reality with Tableau 2022. You''ll learn how to organize data and structure analysis with stories in mind, embrace exploration and visual discovery, and articulate your findings with rich data, well-curated visualizations, and skillfully crafted narrative frameworks. Ryan''s insider tips take you far beyond the basics--and you''ll rely on her expert checklists for years to come. <ul> <li>Communicate more powerfully by applying scientific knowledge of the human brain</li> <li>Get started with the Tableau platform and Tableau Desktop 2022</li> <li>Connect data and quickly prepare it for analysis</li> <li>Ask questions that help you keep data firmly in context</li> <li>Choose the right charts, graphs, and maps for each project--and avoid the wrong ones</li> <li>Craft storyboards that reflect your message and audience</li> <li>Direct attention to what matters most</li> <li>Build data dashboards that guide people towards meaningful outcomes</li> <li>Master advanced visualizations, including timelines, Likert scales, and lollipop charts</li> </ul> This book has only one prerequisite: your desire to communicate insights from data in ways that are memorable and actionable. It''s for executives and professionals sharing important results, students writing reports or presentations, teachers cultivating data literacy, journalists making sense of complex trends. . . . practically everyone! Don''t even have Tableau? Download your free trial of Tableau Desktop and let''s get started!
INGLÊS

Pandas For Everyone - Python Data Analysis

de Daniel Y. Chen

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: <ul> <li>Extended coverage of plotting and the seaborn data visualization library</li> <li>Expanded examples and resources</li> <li>Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries</li> <li>Online bonus material on geopandas, Dask, and creating interactive graphics with Altair</li> </ul> Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. <ul> <li>Work with DataFrames and Series, and import or export data</li> <li>Create plots with matplotlib, seaborn, and pandas</li> <li>Combine data sets and handle missing data</li> <li>Reshape, tidy, and clean data sets so they’re easier to work with</li> <li>Convert data types and manipulate text strings</li> <li>Apply functions to scale data manipulations</li> <li>Aggregate, transform, and filter large data sets with groupby</li> <li>Leverage Pandas’ advanced date and time capabilities</li> <li>Fit linear models using statsmodels and scikit-learn libraries</li> <li>Use generalized linear modeling to fit models with different response variables</li> <li>Compare multiple models to select the "best" one</li> <li>Regularize to overcome overfitting and improve performance</li> <li>Use clustering in unsupervised machine learning</li> </ul>
INGLÊS

Pandas For Everyone - Python Data Analysis

de Daniel Y. Chen

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: <ul> <li>Extended coverage of plotting and the seaborn data visualization library</li> <li>Expanded examples and resources</li> <li>Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries</li> <li>Online bonus material on geopandas, Dask, and creating interactive graphics with Altair</li> </ul> Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. <ul> <li>Work with DataFrames and Series, and import or export data</li> <li>Create plots with matplotlib, seaborn, and pandas</li> <li>Combine data sets and handle missing data</li> <li>Reshape, tidy, and clean data sets so they’re easier to work with</li> <li>Convert data types and manipulate text strings</li> <li>Apply functions to scale data manipulations</li> <li>Aggregate, transform, and filter large data sets with groupby</li> <li>Leverage Pandas’ advanced date and time capabilities</li> <li>Fit linear models using statsmodels and scikit-learn libraries</li> <li>Use generalized linear modeling to fit models with different response variables</li> <li>Compare multiple models to select the "best" one</li> <li>Regularize to overcome overfitting and improve performance</li> <li>Use clustering in unsupervised machine learning</li> </ul>
INGLÊS

Product Analytics - Applied Data Science Techniques For Actionable Consumer Insights

de Joanne Rodrigues

47,69€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Use Product Analytics to Understand and Change Consumer Behavior at Scale Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change, at scale, what people do. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change. This book fills the gaps that many other data science book leave behind: how to start a new data science project; how to conceptualize complex ideas; building metrics from the statistic and demographic fundamentals; projecting consumer populations and material needs for a business; and causal inference beyond simple A/B testing techniques, such as difference-in-difference, regression discontinuity, propensity score matching, and uplift modelling. Writing for entrepreneurs, product managers, marketers, and other business analytics professionals, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in the R programming language, and getting answers you can trust. Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits. <ul> <li>Develop core metrics and effective KPIs for user analytics in any web product</li> <li>Truly understand statistical inference, and the differences between correlation and causation</li> <li>Build intuitive predictive models to capture user behavior in products</li> <li>Tease out causal effects from observational data using modern, quasi-experimental designs and statistical matching </li> <li>Improve response through uplift modeling and other sophisticated targeting methods</li> <li>Project business costs and product population changes via advanced demographic techniques</li> </ul> <div> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside the book for details. </div>
INGLÊS

Foundational Python For Data Science

de Kennedy Behrman

47,69€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 <div> Learn all the foundational Python you''ll need to solve real data science problems </div> <div> </div> <div> </div> <div> Data science and machine learning--two of the world''s hottest fields--are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world''s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. </div> <div> </div> <div> </div> <div> Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you''ve learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. </div> <div> </div> <div> <ul> <li>Master Google colab notebook Data Science programming</li> <li>Manipulate data with popular Python libraries such as: pandas and numpy</li> <li>Apply Python Data Science recipes to real world projects</li> <li>Learn functional programming essentials unique to Data Science</li> <li>Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project</li> </ul> </div> <div> </div> <div> Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more--all created with colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software. </div> <div> </div> <div> </div>
INGLÊS

Product Analytics - Applied Data Science Techniques For Actionable Consumer Insights

de Joanne Rodrigues

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Use Product Analytics to Understand Consumer Behavior and Change It at Scale Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change what people do at scale. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change. Writing for entrepreneurs, product managers/marketers, and other business practitioners, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in R, and getting answers you can trust. <ul> <li>Develop core metrics and effective KPIs for user analytics in any web product</li> <li>Truly understand statistical inference, and the differences between correlation and causation</li> <li>Conduct more effective A/B tests</li> <li>Build intuitive predictive models to capture user behavior in products</li> <li>Use modern, quasi-experimental designs and statistical matching to tease out causal effects from observational data</li> <li>Improve response through uplift modeling and other sophisticated targeting methods</li> <li>Project business costs/subgroup population changes via advanced demographic projection</li> </ul>Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Visual Data Storytelling With Tableau

de Lindy Ryan

42,39€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Tell Insightful, Actionable Business Stories with Tableau, the World’s Leading Data Visualization Tool! Visual Data Storytelling with Tableau brings together knowledge, context, and hands-on skills for telling powerful, actionable data stories with Tableau. This full-color guide shows how to organize data and structure analysis with storytelling in mind, embrace exploration and visual discovery, and articulate findings with rich data, carefully curated visualizations, and skillfully crafted narrative. You don’t need any visualization experience. Each chapter illuminates key aspects of design practice and data visualization, and guides you step-by-step through applying them in Tableau. Through realistic examples and classroom-tested exercises, Professor Lindy Ryan helps you use Tableau to analyze data, visualize it, and help people connect more intuitively and emotionally with it. Whether you’re an analyst, executive, student, instructor, or journalist, you won’t just master the tools: you’ll learn to craft data stories that make an immediate impact--and inspire action. Learn how to: • Craft more powerful stories by blending data science, genre, and visual design • Ask the right questions upfront to plan data collection and analysis • Build storyboards and choose charts based on your message and audience • Direct audience attention to the points that matter most • Showcase your data stories in high-impact presentations • Integrate Tableau storytelling throughout your business communication • Explore case studies that show what to do--and what not to do • Discover visualization best practices, tricks, and hacks you can use with any tool • Includes coverage up through Tableau 10
INGLÊS

Foundations Of Deep Reinforcement Learning - Theory And Practice In Python

de Laura Graesser e Wah Loon Keng

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. <div> Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. </div> <div> </div> <div> This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. </div> <ul> <li>Understand each key aspect of a deep RL problem</li> <li>Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)</li> <li>Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)</li> <li>Understand how algorithms can be parallelized synchronously and asynchronously</li> <li>Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work</li> <li>Explore algorithm benchmark results with tuned hyperparameters</li> <li>Understand how deep RL environments are designed</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. <div></div>
INGLÊS

Foundations Of Deep Reinforcement Learning - Theory And Practice In Python

de Laura Graesser e Wah Loon Keng

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics. <div> Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. </div> <div> </div> <div> This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. </div> <ul> <li>Understand each key aspect of a deep RL problem</li> <li>Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)</li> <li>Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)</li> <li>Understand how algorithms can be parallelized synchronously and asynchronously</li> <li>Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work</li> <li>Explore algorithm benchmark results with tuned hyperparameters</li> <li>Understand how deep RL environments are designed</li> </ul> <div> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. </div> <div></div>
INGLÊS

Deep Learning Illustrated - A Visual, Interactive Guide To Artificial Intelligence

de Aglae Bassens, Grant Beyleveld e Jon Krohn

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 <blockquote> <div style=""> "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." - Tim Urban, author of Wait But Why </div> </blockquote> Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn-with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens-presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. <ul> <li>Discover what makes deep learning systems unique, and the implications for practitioners</li> <li>Explore new tools that make deep learning models easier to build, use, and improve</li> <li>Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more</li> <li>Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Deep Learning Illustrated - A Visual, Interactive Guide To Artificial Intelligence

de Aglae Bassens, Grant Beyleveld e Jon Krohn

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 <div></div> <div style=""></div> <blockquote> <div style=""> "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." — Tim Urban, author of Wait But Why </div> </blockquote> <div> Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn—with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens—presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. <ul> <li>Discover what makes deep learning systems unique, and the implications for practitioners</li> <li>Explore new tools that make deep learning models easier to build, use, and improve</li> <li>Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more</li> <li>Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. </div>
INGLÊS

Machine Learning With Python For Everyone

de Mark Fenner

42,39€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. <ul> <li>Understand machine learning algorithms, models, and core machine learning concepts</li> <li>Classify examples with classifiers, and quantify examples with regressors</li> <li>Realistically assess performance of machine learning systems</li> <li>Use feature engineering to smooth rough data into useful forms</li> <li>Chain multiple components into one system and tune its performance</li> <li>Apply machine learning techniques to images and text</li> <li>Connect the core concepts to neural networks and graphical models</li> <li>Leverage the Python scikit-learn library and other powerful tools</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Machine Learning With Python For Everyone

de Mark Fenner

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. <ul> <li>Understand machine learning algorithms, models, and core machine learning concepts</li> <li>Classify examples with classifiers, and quantify examples with regressors</li> <li>Realistically assess performance of machine learning systems</li> <li>Use feature engineering to smooth rough data into useful forms</li> <li>Chain multiple components into one system and tune its performance</li> <li>Apply machine learning techniques to images and text</li> <li>Connect the core concepts to neural networks and graphical models</li> <li>Leverage the Python scikit-learn library and other powerful tools</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
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Machine Learning In Production - Developing And Optimizing Data Science Workflows And Applications

de Adam Kelleher e Andrew Kelleher

38,15€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Foundational Hands-On Skills for Succeeding with Real Data Science Projects <blockquote> This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. -From the Foreword by Paul Dix, series editor </blockquote> Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent "accidental data scientists" with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. <ul> <li>Leverage agile principles to maximize development efficiency in production projects</li> <li>Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life</li> <li>Start with simple heuristics and improve them as your data pipeline matures</li> <li>Avoid bad conclusions by implementing foundational error analysis techniques</li> <li>Communicate your results with basic data visualization techniques</li> <li>Master basic machine learning techniques, starting with linear regression and random forests</li> <li>Perform classification and clustering on both vector and graph data</li> <li>Learn the basics of graphical models and Bayesian inference</li> <li>Understand correlation and causation in machine learning models</li> <li>Explore overfitting, model capacity, and other advanced machine learning techniques</li> <li>Make informed architectural decisions about storage, data transfer, computation, and communication</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Data Science Foundations Tools And Techniques

de Joel Ross e Michael Freeman

46,36€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 <div usercontent"=""> The Foundational Hands-On Skills You Need to Dive into Data Science <blockquote> "Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills." -From the foreword by Jared Lander, series editor </blockquote> Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to <ul> <li>Install your complete data science environment, including R and RStudio</li> <li>Manage projects efficiently, from version tracking to documentation</li> <li>Host, manage, and collaborate on data science projects with GitHub</li> <li>Master R language fundamentals: syntax, programming concepts, and data structures</li> <li>Load, format, explore, and restructure data for successful analysis</li> <li>Interact with databases and web APIs</li> <li>Master key principles for visualizing data accurately and intuitively</li> <li>Produce engaging, interactive visualizations with ggplot and other R packages</li> <li>Transform analyses into sharable documents and sites with R Markdown</li> <li>Create interactive web data science applications with Shiny</li> <li>Collaborate smoothly as part of a data science team</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. </div>
INGLÊS

Data Science Foundations Tools And Techniques

de Joel Ross e Michael Freeman

35,76€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Foundational Hands-On Skills You Need to Dive into Data Science <blockquote> "Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills." -From the foreword by Jared Lander, series editor </blockquote> Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to <ul> <li>Install your complete data science environment, including R and RStudio</li> <li>Manage projects efficiently, from version tracking to documentation</li> <li>Host, manage, and collaborate on data science projects with GitHub</li> <li>Master R language fundamentals: syntax, programming concepts, and data structures</li> <li>Load, format, explore, and restructure data for successful analysis</li> <li>Interact with databases and web APIs</li> <li>Master key principles for visualizing data accurately and intuitively</li> <li>Produce engaging, interactive visualizations with ggplot and other R packages</li> <li>Transform analyses into sharable documents and sites with R Markdown</li> <li>Create interactive web data science applications with Shiny</li> <li>Collaborate smoothly as part of a data science team</li> </ul> Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
INGLÊS

Visual Data Storytelling With Tableau

de Lindy Ryan

38,15€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 Tell Insightful, Actionable Business Stories with Tableau, the World’s Leading Data Visualization Tool! Visual Data Storytelling with Tableau brings together knowledge, context, and hands-on skills for telling powerful, actionable data stories with Tableau. This full-color guide shows how to organize data and structure analysis with storytelling in mind, embrace exploration and visual discovery, and articulate findings with rich data, carefully curated visualizations, and skillfully crafted narrative. You don’t need any visualization experience. Each chapter illuminates key aspects of design practice and data visualization, and guides you step-by-step through applying them in Tableau. Through realistic examples and classroom-tested exercises, Professor Lindy Ryan helps you use Tableau to analyze data, visualize it, and help people connect more intuitively and emotionally with it. Whether you’re an analyst, executive, student, instructor, or journalist, you won’t just master the tools: you’ll learn to craft data stories that make an immediate impact--and inspire action. Learn how to: • Craft more powerful stories by blending data science, genre, and visual design • Ask the right questions upfront to plan data collection and analysis • Build storyboards and choose charts based on your message and audience • Direct audience attention to the points that matter most • Showcase your data stories in high-impact presentations • Integrate Tableau storytelling throughout your business communication • Explore case studies that show what to do--and what not to do • Discover visualization best practices, tricks, and hacks you can use with any tool • Includes coverage up through Tableau 10
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Pandas For Everyone - Python Data Analysis

de Daniel Y. Chen

37,09€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. <ul> <li>Work with DataFrames and Series, and import or export data</li> <li>Create plots with matplotlib, seaborn, and pandas</li> <li>Combine datasets and handle missing data</li> <li>Reshape, tidy, and clean datasets so they’re easier to work with</li> <li>Convert data types and manipulate text strings</li> <li>Apply functions to scale data manipulations</li> <li>Aggregate, transform, and filter large datasets with groupby</li> <li>Leverage Pandas’ advanced date and time capabilities</li> <li>Fit linear models using statsmodels and scikit-learn libraries</li> <li>Use generalized linear modeling to fit models with different response variables</li> <li>Compare multiple models to select the "best"</li> <li>Regularize to overcome overfitting and improve performance</li> <li>Use clustering in unsupervised machine learning</li> </ul>
INGLÊS

Pandas For Everyone - Python Data Analysis

de Daniel Y. Chen

37,09€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. <ul> <li>Work with DataFrames and Series, and import or export data</li> <li>Create plots with matplotlib, seaborn, and pandas</li> <li>Combine datasets and handle missing data</li> <li>Reshape, tidy, and clean datasets so they’re easier to work with</li> <li>Convert data types and manipulate text strings</li> <li>Apply functions to scale data manipulations</li> <li>Aggregate, transform, and filter large datasets with groupby</li> <li>Leverage Pandas’ advanced date and time capabilities</li> <li>Fit linear models using statsmodels and scikit-learn libraries</li> <li>Use generalized linear modeling to fit models with different response variables</li> <li>Compare multiple models to select the "best"</li> <li>Regularize to overcome overfitting and improve performance</li> <li>Use clustering in unsupervised machine learning</li> </ul>
INGLÊS

R For Everyone - Advanced Analytics And Graphics

de Jared P. Lander

51,66€ + 10% EM CARTÃO

DISPONIBILIDADE IMEDIATA

  Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes <ul> <li>Explore R, RStudio, and R packages</li> <li>Use R for math: variable types, vectors, calling functions, and more</li> <li>Exploit data structures, including data.frames, matrices, and lists</li> <li>Read many different types of data</li> <li>Create attractive, intuitive statistical graphics</li> <li>Write user-defined functions</li> <li>Control program flow with if, ifelse, and complex checks</li> <li>Improve program efficiency with group manipulations</li> <li>Combine and reshape multiple datasets</li> <li>Manipulate strings using R’s facilities and regular expressions</li> <li>Create normal, binomial, and Poisson probability distributions</li> <li>Build linear, generalized linear, and nonlinear models</li> <li>Program basic statistics: mean, standard deviation, and t-tests</li> <li>Train machine learning models</li> <li>Assess the quality of models and variable selection</li> <li>Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods</li> <li>Analyze univariate and multivariate time series data</li> <li>Group data via K-means and hierarchical clustering</li> <li>Prepare reports, slideshows, and web pages with knitr</li> <li>Display interactive data with RMarkdown and htmlwidgets</li> <li>Implement dashboards with Shiny</li> <li>Build reusable R packages with devtools and Rcpp</li> </ul> Register your product at <a href="http://informit.com/register" target="" title="">informit.com/register</a> for convenient access to downloads, updates, and corrections as they become available.
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