Deep Learning Essentials
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R Deep Learning Essentials
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Author : Mark Hodnett
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-24
R Deep Learning Essentials written by Mark Hodnett and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-24 with Computers categories.
Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.
Artificial Intelligence And Deep Learning Essentials
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Author : James Russell
language : en
Publisher: Independently Published
Release Date : 2018-05-12
Artificial Intelligence And Deep Learning Essentials written by James Russell and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-12 with categories.
Get to grips with the essentials of deep learning by leveraging the power of PythonKey Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who This Book Is For Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python. Table of Contents 1. What is artificial intelligence 2. Why is the artificial intelligence important ? 3. Applications of Machine Learning 4. Semantics, Probability and IA 5. Numerical Computation 6. Sequence Modeling, Recurrent and Recursive Nets 7. Autoencoders 8. Markov Chains, Monte Carlo Methods, and Machine Learning
Deep Learning Essentials
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Author : Anurag Bhardwaj
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-30
Deep Learning Essentials written by Anurag Bhardwaj and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-30 with Computers categories.
Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.
Machine Learning Essentials And Applications
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Author : Mrs. N. Jayasri
language : en
Publisher: RK Publication
Release Date : 2024-07-27
Machine Learning Essentials And Applications written by Mrs. N. Jayasri and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Computers categories.
Machine Learning Essentials and Applications a comprehensive of machine learning's core principles, methodologies, and real-world applications. This book is designed for both beginners and professionals, covering essential topics like supervised and unsupervised learning, neural networks, and deep learning. With clear explanations and practical examples, it connects theory to practice, showcasing machine learning’s impact across industries such as healthcare, finance, and technology. Ideal for readers seeking foundational knowledge and insights into the transformative potential of machine learning in various fields.
Generative Ai Deep Learning Essentials
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Author : Dr Sophia Anders
language : en
Publisher: Independently Published
Release Date : 2025-07-05
Generative Ai Deep Learning Essentials written by Dr Sophia Anders and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-05 with Computers categories.
The Ultimate Generative AI Mastery Collection: 3 Books in 1 Learn ChatGPT, Deep Learning, and AI Tools Step by Step-No Tech Skills Needed Unlock the secrets of Artificial Intelligence with this complete, beginner-friendly guide to mastering the tools shaping our world. Inside this powerful collection, you'll discover: ✅ Generative AI Demystified: Learn how ChatGPT and other cutting-edge models work-and how you can harness them in your projects. ✅ Deep Learning Made Simple: Step-by-step tutorials that take you from perceptrons to convolutional and recurrent neural networks. ✅ Practical Hands-On Projects: Build real AI solutions for image recognition, sentiment analysis, and more, with ready-to-use Python examples. ✅ Professional Workflows: Master TensorFlow, PyTorch, Keras, and Scikit-Learn to create production-ready models. ✅ Ethics and Responsible AI: Understand how to build trustworthy systems that protect privacy and fairness. Whether you're a student, entrepreneur, or lifelong learner, this collection gives you the tools and confidence to excel in the AI-driven future. No coding background? No problem. Every concept is explained clearly-so you can start building real-world AI solutions today. Transform your skills. Future-proof your career. Discover what's possible with Generative AI. Scroll up and grab your copy now!
Deep Learning Essentials
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Author : Aeronis Krynn
language : en
Publisher: Independently Published
Release Date : 2025-05-24
Deep Learning Essentials written by Aeronis Krynn and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-24 with Computers categories.
So, you want to master deep learning? Not just dip your toes in the neural network kiddie pool, but really dive in-arms flailing, tensors flying, activation functions lighting the way like a rave in your GPU? You're in the right place. Welcome to Deep Learning Essentials: Master Neural Networks for AI-your no-fluff, fun-to-read, gloriously hands-on guide to everything you need to know about deep learning, written by me, Aeronis Krynn, your guide through the generative AI wilderness. This book is part of The Generative AI Blueprint series, a collection of books for future-focused creators, coders, and slightly-caffeinated AI enthusiasts who want to build, not just read. In this book, we break down the complex world of deep learning into bite-sized, brain-friendly pieces-starting from the "What even is deep learning?" basics to building real models using PyTorch and TensorFlow. We dig into the inner workings of neural networks: layers, weights, activation functions, and everyone's favorite algorithmic soap opera-backpropagation. You'll learn how to train these digital beasts, optimize them, debug when things go off the rails (and they will), and finally, deploy your models like a pro. But wait-there's more! Because this book doesn't live in isolation like a lonely dropout neuron. It's one part of a glorious AI-fueled saga. If you're totally new to this world, start with Generative AI for Beginners: A Complete Introduction-your gateway to understanding how machines went from calculators to creative powerhouses. Then sharpen your code-fu with Python for AI, where you'll learn to build generative models from scratch. Already a little dangerous? Jump into Autoencoders & VAEs or go full digital artist with Stable Diffusion & AI Art. Want to make chatbots smarter than your cousin? Transformers & GPT is calling your name. Deep Learning Essentials is the engine room of the AI factory. It's where the black magic happens, where the models actually learn, and where you-yes, you-get to play architect to some of the most powerful tech on the planet. And don't worry if you're not a math wizard. We keep things light, humorous, and clear. Think of it like a Netflix show about AI, but with code and less dramatic pauses. By the end of this book, you won't just understand deep learning-you'll be building models, tweaking hyperparameters like a mad genius, and applying your skills to real-world challenges, from image recognition to sequence modeling to generative tasks. Whether you want to create smart apps, dive into AI research, or just impress people at parties by explaining what a Transformer really is (hint: not a robot), this book is your launchpad. Let's build something awesome. Part of The Generative AI Blueprint series: Generative AI for Beginners Python for AI Autoencoders & VAEs GANs in Action Transformers & GPT Stable Diffusion & AI Art Multimodal AI Fine-Tuning & Deploying AI Models The Future of Generative AI
Machine Learning Essentials You Always Wanted To Know
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Author : Dhairya Parikh
language : en
Publisher: Vibrant Publishers
Release Date : 2025-07-04
Machine Learning Essentials You Always Wanted To Know written by Dhairya Parikh and has been published by Vibrant Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-04 with Business & Economics categories.
· Covers key algorithms and techniques · Ideal for students and professionals · Hands-on implementation included Master the fundamentals of ML and take the first step towards a career in AI! In today’s rapidly evolving world, machine learning (ML) is no longer just for researchers or data scientists. From personalized recommendations on streaming platforms to fraud detection in banking, ML powers many aspects of our daily lives. As industries increasingly adopt AI-driven solutions, learning machine learning has become a valuable skill. Yet, many find the subject overwhelming, often intimidated by its mathematical complexity. That’s where Machine Learning Essentials You Always Wanted to Know (Machine Learning Essentials) comes in. This beginner-friendly guide offers a structured, step-by-step approach to understanding machine learning concepts without unnecessary jargon. Whether you are a student, a professional looking to transition into AI, or simply curious about how machines learn, this book provides a clear and practical roadmap to mastering ML. Authored by Dhairya Parikh, an experienced data engineer who returned to academia to refine his expertise, this book bridges the gap between theory and real-world application. It simplifies the core concepts of ML, breaking them down into digestible explanations paired with hands-on coding exercises to help you apply what you learn. What You’ll Learn: · The fundamentals of machine learning and how it powers modern technology · The three key types of ML—Supervised, Unsupervised, and Reinforcement Learning · How to combine algorithms, data, and models to develop AI-driven solutions · Practical coding techniques to build and implement machine learning models Part of Vibrant Publishers’ Self-Learning Management Series, this book serves as a valuable guide for building machine learning skills, enhancing your expertise, and advancing your career in AI and data science.
Machine Learning Essentials A Practical Guide To Building Accurate And Reliable Models
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Author : Devansh Dhiman
language : en
Publisher: Devansh Dhiman
Release Date : 2023-05-01
Machine Learning Essentials A Practical Guide To Building Accurate And Reliable Models written by Devansh Dhiman and has been published by Devansh Dhiman this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-01 with Business & Economics categories.
Machine learning is a powerful tool for making accurate predictions and improving decision-making based on data-driven insights. However, building accurate and reliable machine learning models requires a thorough understanding of the machine learning workflow, from data preprocessing and exploration to model training and deployment. In this ebook, we provide a practical guide to machine learning essentials, covering everything from the basics of supervised and unsupervised learning to deep learning and model deployment. We explore common machine learning algorithms, including decision trees, support vector machines, and neural networks, and provide examples of how they can be used in real-world applications. We also delve into data preprocessing and exploration, discussing techniques for cleaning, transforming, and scaling data to make it suitable for analysis, and exploring ways to gain insights into the properties and relationships of the data. We discuss best practices for model training and evaluation, and explore strategies for deploying and maintaining machine learning models in a production environment. Whether you're an experienced data scientist or just starting out, this ebook provides a comprehensive guide to building accurate and reliable machine learning models that can transform your business and improve decision-making based on data-driven insights.
Python Machine Learning Essentials
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Author : Bernard Baah
language : en
Publisher: Independently Published
Release Date : 2024-03-22
Python Machine Learning Essentials written by Bernard Baah and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.
"Python Machine Learning Essentials" by Bernard Baah is your ultimate guide to mastering machine learning concepts and techniques using Python. Whether you're a beginner or an experienced programmer, this book equips you with the knowledge and skills needed to understand and apply machine learning algorithms effectively. With a comprehensive approach, Bernard Baah takes you through the fundamentals of machine learning, covering Python basics, data preprocessing, exploratory data analysis, supervised and unsupervised learning, neural networks, natural language processing, model deployment, and more. Each chapter is filled with practical examples, code snippets, and hands-on exercises to reinforce your learning and deepen your understanding. As the founder of Filly Coder (https: //fillycoder.com), Bernard Baah brings years of experience in machine learning and software development to this book. His expertise and passion for teaching shine through, making complex concepts accessible and understandable for readers of all levels. Whether you're a data scientist, developer, or aspiring AI enthusiast, "Python Machine Learning Essentials" is your go-to resource for mastering machine learning with Python. Dive into the world of machine learning and unlock the potential to build intelligent applications with confidence. Get your copy of "Python Machine Learning Essentials" today and embark on your journey to becoming a proficient machine learning practitioner
R Deep Learning Essentials
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Author : Joshua F. Wiley
language : en
Publisher:
Release Date : 2016-03-29
R Deep Learning Essentials written by Joshua F. Wiley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-29 with Computers categories.
Build automatic classification and prediction models using unsupervised learningAbout This Book- Harness the ability to build algorithms for unsupervised data using deep learning concepts with R- Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models- Build models relating to neural networks, prediction and deep predictionWho This Book Is ForThis book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts.What You Will Learn- Set up the R package H2O to train deep learning models- Understand the core concepts behind deep learning models- Use Autoencoders to identify anomalous data or outliers- Predict or classify data automatically using deep neural networks- Build generalizable models using regularization to avoid overfitting the training dataIn DetailDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.Style and approachThis book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.