Mastering Ai Model Training

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Mastering AI model training

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 272 pages
Book Rating : 4.8/5 (549 download)

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Book Synopsis Mastering AI model training by : Cybellium Ltd

Download or read book Mastering AI model training written by Cybellium Ltd and published by Cybellium Ltd. This book was released on 2023-09-05 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you ready to take your AI training skills to the next level? In "Mastering AI Model Training" by Kris Hermans, you'll embark on a transformative journey that will empower you to train highly accurate and efficient artificial intelligence models. Uncover Advanced Techniques and Best Practices As AI continues to revolutionize industries, the ability to train powerful and optimized models is paramount. In this comprehensive guide, Kris Hermans reveals the secrets to mastering AI model training. Explore advanced techniques, cutting-edge algorithms, and industry best practices that will propel your AI training expertise to new heights. Become an Expert in Training AI Models Whether you're a seasoned data scientist or a passionate AI enthusiast, this book provides a structured approach to mastering AI model training. Kris Hermans demystifies complex concepts and presents them in a clear and practical manner. Through real-world examples and hands-on exercises, you'll develop the skills and intuition necessary to train AI models that achieve exceptional performance. From Fundamentals to Advanced Topics "Mastering AI Model Training" covers the full spectrum of AI training, starting from the basics of data preprocessing and feature engineering and progressing to advanced topics such as transfer learning, hyperparameter optimization, and model compression. Gain a deep understanding of different training algorithms and architectures, and learn how to adapt them to various domains and use cases. Optimize Training for Performance and Efficiency Discover strategies for improving model performance, reducing training time, and optimizing resource utilization. Explore techniques for handling large datasets, distributed training, and leveraging hardware accelerators such as GPUs and TPUs. With Kris Hermans as your guide, you'll learn how to train models that deliver superior results while maximizing computational efficiency. Practical Applications and Real-World Case Studies Immerse yourself in practical applications of AI model training across industries such as healthcare, finance, manufacturing, and more. Dive into captivating case studies that demonstrate how AI training is transforming businesses and driving innovation. Gain insights into the challenges faced by organizations and learn how they leverage AI training techniques to gain a competitive edge. Ethical Considerations and Responsible AI With great power comes great responsibility. "Mastering AI Model Training" addresses the ethical considerations associated with AI training and highlights the importance of responsible AI practices. Learn how to mitigate biases, ensure fairness, and navigate ethical challenges to build AI models that are not only accurate and efficient but also ethical and trustworthy.

Mastering AI ethics

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 149 pages
Book Rating : 4.8/5 (549 download)

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Book Synopsis Mastering AI ethics by : Cybellium Ltd

Download or read book Mastering AI ethics written by Cybellium Ltd and published by Cybellium Ltd. This book was released on 2023-09-05 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an era shaped by the rapid evolution of artificial intelligence, grappling with the ethical dimensions of AI technologies has become an imperative. "Mastering AI Ethics" is a groundbreaking guide that delves deep into the complexities of AI ethics, equipping readers with the insights required to navigate the ethical challenges posed by AI innovations. About the Book: In this thought-provoking book, readers are invited to explore the intricate web of ethical considerations surrounding AI development, deployment, and societal impact. With real-world examples, case studies, and actionable frameworks, "Mastering AI Ethics" empowers readers to make informed decisions and contribute to a future where AI serves the greater good. Key Features: Foundations of Ethical AI: The book lays a strong foundation by demystifying the core concepts that underpin AI ethics. Readers will develop a clear understanding of how ethical considerations intersect with AI technologies and why these intersections are crucial. Tackling Complex Ethical Dilemmas: Through a series of real-world scenarios, readers will grapple with intricate ethical dilemmas presented by AI. The book guides readers in analyzing and evaluating these scenarios, enabling them to cultivate the critical thinking skills needed to confront ethical challenges head-on. Frameworks for Ethical Decision-Making: "Mastering AI Ethics" introduces readers to practical frameworks and models designed to facilitate ethical decision-making in AI contexts. These frameworks empower readers to weigh conflicting interests, anticipate potential harms, and arrive at ethically sound solutions. Promoting Transparency and Accountability: The book delves into the concepts of transparency and accountability in AI development and deployment. Readers will discover how to foster transparency, hold AI systems accountable, and ensure responsible use of AI technologies. Societal Implications of AI: By examining broader societal implications, the book explores how AI influences areas such as privacy, bias, fairness, and social justice. Readers will gain insights into how AI technologies can amplify existing inequalities and how to design AI systems that mitigate these effects. Collaborative Ethical Practices: "Mastering AI Ethics" underscores the significance of collaborative efforts in shaping AI ethics. Readers will learn how interdisciplinary collaboration involving ethicists, technologists, policymakers, and stakeholders can drive more ethical AI development and deployment. Exploring Future Ethical Challenges: As AI continues to evolve, so do its ethical considerations. The book provides a forward-looking perspective on emerging trends in AI ethics, from the ethical implications of AI in healthcare to the challenges posed by autonomous systems and AI-driven decision-making. Who Should Read This Book: "Mastering AI Ethics" is an indispensable resource for AI practitioners, data scientists, ethicists, policymakers, and anyone concerned with the ethical implications of AI technologies. Whether you're an AI researcher aiming to integrate ethics into your work, a business leader exploring responsible AI implementation, or a curious citizen intrigued by AI's impact on society, this book equips you with the tools to engage in meaningful discussions and drive ethical change in the AI landscape.

Mastering Machine Learning Algorithms

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1838821910
Total Pages : 799 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Mastering Machine Learning Algorithms by : Giuseppe Bonaccorso

Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Mastering PyTorch

Author : Ashish Ranjan Jha
Publisher : Packt Publishing Ltd
ISBN 13 : 1789616409
Total Pages : 450 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Mastering PyTorch by : Ashish Ranjan Jha

Download or read book Mastering PyTorch written by Ashish Ranjan Jha and published by Packt Publishing Ltd. This book was released on 2021-02-12 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.

Mastering AI

Author : Jeremy Kahn
Publisher : Simon and Schuster
ISBN 13 : 1668053349
Total Pages : 336 pages
Book Rating : 4.6/5 (68 download)

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Book Synopsis Mastering AI by : Jeremy Kahn

Download or read book Mastering AI written by Jeremy Kahn and published by Simon and Schuster. This book was released on 2024-07-09 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Fortune magazine journalist draws on his expertise and extensive contacts among the companies and scientists at the forefront of artificial intelligence to offer dramatic predictions of AI’s impact over the next decade, from reshaping our economy and the way we work, learn, and create to unknitting our social fabric, jeopardizing our democracy, and fundamentally altering the way we think. Within the next five years, Jeremy Kahn predicts, AI will disrupt almost every industry and enterprise, with vastly increased efficiency and productivity. It will restructure the workforce, making AI copilots a must for every knowledge worker. It will revamp education, meaning children around the world can have personal, portable tutors. It will revolutionize health care, making individualized, targeted pharmaceuticals more affordable. It will compel us to reimagine how we make art, compose music, and write and publish books. The potential of generative AI to extend our skills, talents, and creativity as humans is undeniably exciting and promising. But while this new technology has a bright future, it also casts a dark and fearful shadow. AI will provoke pervasive, disruptive, potentially devastating knock-on effects. Leveraging his unrivaled access to the leaders, scientists, futurists, and others who are making AI a reality, Kahn will argue that if not carefully designed and vigilantly regulated AI will deepen income inequality, depressing wages while imposing winner-take-all markets across much of the economy. AI risks undermining democracy, as truth is overtaken by misinformation, racial bias, and harmful stereotypes. Continuing a process begun by the internet, AI will rewire our brains, likely inhibiting our ability to think critically, to remember, and even to get along with one another—unless we all take decisive action to prevent this from happening. Much as Michael Lewis’s classic The New New Thing offered a prescient, insightful, and eminently readable account of life inside the dot-com bubble, Mastering AI delivers much-needed guidance for anyone eager to understand the AI boom—and what comes next.

Mastering Deep Learning

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 240 pages
Book Rating : 4.8/5 (75 download)

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Book Synopsis Mastering Deep Learning by : Cybellium Ltd

Download or read book Mastering Deep Learning written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Power of Neural Networks for Intelligent Solutions In the landscape of artificial intelligence and machine learning, deep learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering you to create intelligent solutions that drive innovation. About the Book: As the capabilities of technology expand, deep learning emerges as a transformative approach that unlocks the potential of artificial intelligence. "Mastering Deep Learning" offers a comprehensive exploration of this cutting-edge field—an indispensable toolkit for data scientists, engineers, and enthusiasts. This book caters to both beginners and experienced learners aiming to excel in deep learning concepts, algorithms, and applications. Key Features: Deep Learning Fundamentals: Begin by understanding the core principles of deep learning. Learn about neural networks, activation functions, and backpropagation—the building blocks of the subject. Deep Neural Architectures: Dive into the world of deep neural architectures. Explore techniques for building and designing different types of neural networks, including feedforward, convolutional, and recurrent networks. Training and Optimization: Grasp the art of training deep neural networks. Understand techniques for weight initialization, gradient descent, and optimization algorithms to ensure efficient learning. Natural Language Processing: Explore deep learning applications in natural language processing. Learn how to process and understand text, sentiment analysis, and language generation. Computer Vision: Understand the significance of deep learning in computer vision. Explore techniques for image classification, object detection, and image generation. Reinforcement Learning: Delve into the realm of reinforcement learning. Explore techniques for training agents to interact with environments and make intelligent decisions. Transfer Learning and Pretrained Models: Grasp the power of transfer learning. Learn how to leverage pretrained models and adapt them to new tasks. Real-World Applications: Gain insights into how deep learning is applied across industries. From healthcare to finance, discover the diverse applications of deep neural networks. Why This Book Matters: In an era of rapid technological advancement, mastering deep learning offers a competitive edge. "Mastering Deep Learning" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge concepts, enabling them to create intelligent solutions that drive innovation and redefine possibilities. Unleash the Future of AI: In the landscape of artificial intelligence, deep learning is reshaping technology and innovation. "Mastering Deep Learning" equips you with the knowledge needed to leverage deep neural networks, enabling you to create intelligent solutions that push the boundaries of possibilities. Whether you're a seasoned practitioner or new to the world of deep learning, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering deep learning starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 72 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications by : Anand Vemula

Download or read book Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide dives into the fascinating world of Artificial Intelligence (AI) and its cutting-edge subfield, Generative AI. Designed for beginners and enthusiasts alike, it equips you with the knowledge and skills to navigate the complexities of machine learning and unlock the power of AI for advanced applications. Building a Strong Foundation The journey begins with mastering the fundamentals. You'll explore the different approaches to AI, delve into the history of this revolutionary field, and gain a solid understanding of various subfields like Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Delving into Machine Learning Machine learning, the core of AI's learning ability, takes center stage. You'll grasp the difference between supervised and unsupervised learning paradigms, discover popular algorithms like decision trees and neural networks, and learn the importance of data preparation for optimal model performance. Evaluation metrics become your tools to measure how effectively your models are learning. Unveiling the Power of Deep Learning Get ready to explore the intricate world of Deep Learning, a powerful subset of machine learning inspired by the human brain. Demystify neural networks, the building blocks of deep learning, and dive into specialized architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for handling sequential data. Deep learning frameworks become your allies, simplifying the process of building and deploying complex deep learning models. The Art of Machine Creation: Generative AI The book then shifts its focus to the transformative realm of Generative AI. Here, machines not only learn but create entirely new data. Explore different types of generative models, from autoregressive models to variational autoencoders, and witness their applications in text generation, image synthesis, and even music creation. A Deep Dive into Generative Adversarial Networks (GANs) Among generative models, Generative Adversarial Networks (GANs) have captured the imagination of researchers and the public alike. This chapter delves into the intriguing concept of GANs, where a generator model continuously strives to create realistic data while a discriminator model acts as a critic, ensuring the generated data is indistinguishable from real data. You'll explore the training process, the challenges of taming GANs, and best practices for achieving optimal results. Advanced Applications Across Domains The book then showcases the transformative potential of Generative AI across various domains. Witness the power of text generation with RNNs, explore the ethical considerations surrounding deepfakes, and discover how chatbots are revolutionizing communication. In the visual realm, delve into Deep Dream and Neural Style Transfer algorithms, and witness the creation of realistic images and videos with cutting-edge generative models. Mastering AI and Generative AI empowers you to not only understand these revolutionary technologies but also leverage them for advanced applications. As you embark on this journey, be prepared to unlock the boundless potential of machine creation and shape the future of AI.

Mastering Azure Machine Learning

Author : Christoph Korner
Publisher : Packt Publishing Ltd
ISBN 13 : 1803246790
Total Pages : 624 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Mastering Azure Machine Learning by : Christoph Korner

Download or read book Mastering Azure Machine Learning written by Christoph Korner and published by Packt Publishing Ltd. This book was released on 2022-05-10 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services Key Features Implement end-to-end machine learning pipelines on Azure Train deep learning models using Azure compute infrastructure Deploy machine learning models using MLOps Book Description Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline. What you will learn Understand the end-to-end ML pipeline Get to grips with the Azure Machine Learning workspace Ingest, analyze, and preprocess datasets for ML using the Azure cloud Train traditional and modern ML techniques efficiently using Azure ML Deploy ML models for batch and real-time scoring Understand model interoperability with ONNX Deploy ML models to FPGAs and Azure IoT Edge Build an automated MLOps pipeline using Azure DevOps Who this book is for This book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.

Mastering AI Development

Author : Matthew B Richard
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.8/5 (795 download)

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Book Synopsis Mastering AI Development by : Matthew B Richard

Download or read book Mastering AI Development written by Matthew B Richard and published by Independently Published. This book was released on 2024-02-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harnessing GANs, TensorFlow, and Advanced Techniques for Building, Training, and Deploying Cutting-Edge Models""Mastering AI Development: Harnessing GANs, TensorFlow, and Advanced Techniques for Building, Training, and Deploying Cutting-Edge Models" is a comprehensive guide that delves into the intricate world of artificial intelligence development. From understanding the fundamentals of Generative Adversarial Networks (GANs) to leveraging the power of TensorFlow, this book equips readers with the knowledge and skills needed to create, train, and deploy state-of-the-art AI models. Covering advanced techniques and methodologies, this book serves as an essential resource for developers, researchers, and AI enthusiasts looking to stay at the forefront of AI innovation. With practical examples, case studies, and expert insights, readers will gain a deep understanding of AI development and be empowered to tackle complex challenges in today's rapidly evolving AI landscape. Whether you're a seasoned practitioner or just starting your AI journey, "Mastering AI Development" provides the tools and techniques necessary to unlock the full potential of artificial intelligence

Mastering Generative AI Software Development

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering Generative AI Software Development by : Anand Vemula

Download or read book Mastering Generative AI Software Development written by Anand Vemula and published by Anand Vemula. This book was released on with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mastering Generative AI Software Development equips you to harness the power of generative models, a revolutionary technology capable of creating entirely new and original content. Part 1 establishes a solid foundation. You'll explore the core concepts of generative models, contrasting them with traditional machine learning approaches. We'll delve into the exciting applications of generative AI, from creative content generation like writing and music composition to scientific breakthroughs in drug discovery and material science. The section concludes by discussing both the benefits and challenges associated with this powerful technology. Part 2 guides you through the practical steps of building generative AI systems. We'll tackle data preparation, a crucial stage for ensuring high-quality model training. You'll learn about different data cleaning and augmentation techniques to optimize your data for generative models. Moving on, we'll explore various generative model architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The section delves into the training process, equipping you with the knowledge to choose appropriate loss functions and optimizers for optimal model performance. We'll also explore techniques for monitoring and debugging the training process to ensure successful model development. Part 3 showcases the vast potential of generative AI software across diverse industries. We'll explore how generative models are revolutionizing creative fields, enabling artists and writers to generate new content and explore innovative avenues. Beyond the realm of creativity, we'll delve into the transformative role of generative AI in scientific research, accelerating drug discovery and material design processes. The section concludes by exploring additional applications like data augmentation and natural language processing tasks such as machine translation and chatbot development. Part 4 paves the way for the future. We'll discuss the ethical considerations surrounding generative AI development, particularly the potential for bias and the misuse of realistic content generation. The section concludes by exploring cutting-edge advancements like explainable generative models and the ever-expanding real-world applications of this technology. By the end of this comprehensive guide, you'll possess a thorough understanding of generative AI software development, empowering you to participate in shaping the future of this rapidly evolving field.

Advanced Techniques For Using AI to Master Crypto

Author : Jeffery W Long
Publisher : Jeffery William Long
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4./5 ( download)

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Book Synopsis Advanced Techniques For Using AI to Master Crypto by : Jeffery W Long

Download or read book Advanced Techniques For Using AI to Master Crypto written by Jeffery W Long and published by Jeffery William Long. This book was released on 2024-08-21 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Techniques For Using AI to Master Crypto Chapter 1: Introduction to Cryptocurrency Introduction to Cryptocurrency Cryptocurrency has emerged as one of the most revolutionary financial innovations of the 21st century. At its core, cryptocurrency represents digital or virtual currencies that rely on cryptographic principles to secure transactions, regulate issuance, and authenticate transfer of assets. Established in 2009 with the introduction of Bitcoin by an enigmatic figure known as Satoshi Nakamoto, cryptocurrency has since evolved into a multifaceted domain encompassing various aspects of finance and technology. Cryptocurrency owes its existence to the failure and limitations of traditional financial systems. In 2008, the financial crisis exposed the inherent vulnerabilities of centralized banking and the mistrust surrounding monetary institutions. It was against this backdrop that Bitcoin, the first decentralized cryptocurrency, was conceived. By implementing blockchain technology, Bitcoin established the foundation for a new kind of currency free from the control of central authorities and immune to the pitfalls of traditional financial institutions. The impact of cryptocurrencies on modern financial systems cannot be overstated. Cryptocurrencies facilitate peer-to-peer transactions independent of intermediaries, bringing about cost efficiency and speed in transferring funds. As the public becomes increasingly aware of the benefits and potential of cryptocurrencies, the landscape of global finance is undergoing a transformative shift, heralding the advent of a decentralized economy.

The AI Playbook

Author : Eric Siegel
Publisher : MIT Press
ISBN 13 : 0262378132
Total Pages : 255 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis The AI Playbook by : Eric Siegel

Download or read book The AI Playbook written by Eric Siegel and published by MIT Press. This book was released on 2024-02-06 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it. “Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.” —Scott Galloway, NYU Stern Professor of Marketing; bestselling author of The Four “An antidote to today’s relentless AI hype—why some AI initiatives thrive while others fail and what it takes for companies and people to succeed.” —Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology—but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals, and it establishes a sorely needed strategic framework for data professionals. Beyond detailing the practice, this book also upskills business professionals—painlessly. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need to lead or participate in machine learning projects, end to end. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative—getting them right paves the way for machine learning’s value-driven deployment. What kind of AI does this book cover? The buzzword AI can mean many things, but this book is about machine learning, which is a central basis for—and what many mean by—AI. To be specific, this book covers the most vital use cases of machine learning, those designed to improve a wide range of business operations.

The AI Book

Author : Ivana Bartoletti
Publisher : John Wiley & Sons
ISBN 13 : 1119551900
Total Pages : 304 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis The AI Book by : Ivana Bartoletti

Download or read book The AI Book written by Ivana Bartoletti and published by John Wiley & Sons. This book was released on 2020-06-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important

Mastering Machine Learning on AWS

Author : Dr. Saket S.R. Mengle
Publisher : Packt Publishing Ltd
ISBN 13 : 1789347505
Total Pages : 293 pages
Book Rating : 4.7/5 (893 download)

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Book Synopsis Mastering Machine Learning on AWS by : Dr. Saket S.R. Mengle

Download or read book Mastering Machine Learning on AWS written by Dr. Saket S.R. Mengle and published by Packt Publishing Ltd. This book was released on 2019-05-20 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Converging Minds

Author : Aleksandra Przegalinska
Publisher : CRC Press
ISBN 13 : 1040031080
Total Pages : 169 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Converging Minds by : Aleksandra Przegalinska

Download or read book Converging Minds written by Aleksandra Przegalinska and published by CRC Press. This book was released on 2024-06-17 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This groundbreaking book explores the power of collaborative AI in amplifying human creativity and expertise. Written by two seasoned experts in data analytics, AI, and machine learning, the book offers a comprehensive overview of the creative process behind AI-powered content generation. It takes the reader through a unique collaborative process between human authors and various AI-based topic experts, created, prompted, and fine-tuned by the authors. This book features a comprehensive list of prompts that readers can use to create their own ChatGPT-powered topic experts. By following these expertly crafted prompts, individuals and businesses alike can harness the power of AI, tailoring it to their specific needs and fostering a fruitful collaboration between humans and machines. With real-world use cases and deep insights into the foundations of generative AI, the book showcases how humans and machines can work together to achieve better business outcomes and tackle complex challenges. Social and ethical implications of collaborative AI are covered and how it may impact the future of work and employment. Through reading the book, readers will gain a deep understanding of the latest advancements in AI and how they can shape our world. Converging Minds: The Creative Potential of Collaborative AI is essential reading for anyone interested in the transformative potential of AI-powered content generation and human-AI collaboration. It will appeal to data scientists, machine learning architects, prompt engineers, general computer scientists, and engineers in the fields of generative AI and deep learning. Chapter 1 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [Attribution- No Derivatives (CC-BY -ND)] 4.0 license.

Generative Deep Learning with Python

Author : Cuantum Technologies LLC
Publisher : Packt Publishing Ltd
ISBN 13 : 1836207123
Total Pages : 276 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis Generative Deep Learning with Python by : Cuantum Technologies LLC

Download or read book Generative Deep Learning with Python written by Cuantum Technologies LLC and published by Packt Publishing Ltd. This book was released on 2024-06-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI. Practical coding exercises & interactive assignments to build your own generative models. Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learn Develop a detailed understanding of deep learning fundamentals Implement and train Generative Adversarial Networks (GANs) Create & utilize Variational Autoencoders for data generation Apply autoregressive models for text generation Explore advanced topics & stay ahead in the field of generative AI Analyze and optimize the performance of generative models Who this book is for This course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.

Mastering Machine Learning with Spark 2.x

Author : Alex Tellez
Publisher : Packt Publishing Ltd
ISBN 13 : 1785282417
Total Pages : 334 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Mastering Machine Learning with Spark 2.x by : Alex Tellez

Download or read book Mastering Machine Learning with Spark 2.x written by Alex Tellez and published by Packt Publishing Ltd. This book was released on 2017-08-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment. Style and approach This book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of machine learning, through Spark, in a variety of enterprise-grade systems.