A Practical Approach For Machine Learning And Deep Learning Algorithms

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A Practical Approach For Machine Learning And Deep Learning Algorithms

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Author : Abhishek Kumar Pandey
language : en
Publisher: BPB Publications
Release Date : 2019-09-18

A Practical Approach For Machine Learning And Deep Learning Algorithms written by Abhishek Kumar Pandey and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-18 with Computers categories.

Guide covering topics from machine learning, regression models, neural network to tensor flow DESCRIPTION Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it. KEY FEATURES Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. WHAT WILL YOU LEARN Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning WHO THIS BOOK IS FOR The book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time. Table of Contents _1. Ê Ê Pre-requisite to Machine Learning 2. Ê Ê An introduction to Machine Learning 3. Ê Ê Finding Natural Patterns in Data 4. Ê Ê Building Classification Methods 5. Ê Ê Data Pre-Processing in Python 6. Ê Ê Building Regression Models 7. Ê Ê Creating Neural Networks 8. Ê Ê Introduction to Deep Learning

Advanced Decision Sciences Based On Deep Learning And Ensemble Learning Algorithms

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Author : S. Sumathi
language : en
Publisher: Nova Science Publishers
Release Date : 2021

Advanced Decision Sciences Based On Deep Learning And Ensemble Learning Algorithms written by S. Sumathi and has been published by Nova Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Business & Economics categories.

"Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalize these strategies. The book will benefit information professionals, programmers, consultants, professors, students, and industry experts who seek a variety of real-world illustrations with an implementation based on machine learning algorithms"--

Pro Machine Learning Algorithms

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Author : V Kishore Ayyadevara
language : en
Publisher: Apress
Release Date : 2018-06-30

Pro Machine Learning Algorithms written by V Kishore Ayyadevara and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-30 with Computers categories.

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Hands On Machine Learning With R

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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07

Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Deep Learning

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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10

Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Machine Learning For Beginners

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Author : Dr. Harsh Bhasin
language : en
Publisher: BPB Publications
Release Date : 2023-10-16

Machine Learning For Beginners written by Dr. Harsh Bhasin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-16 with Computers categories.

Learn how to build a complete machine learning pipeline by mastering feature extraction, feature selection, and algorithm training KEY FEATURES ● Develop a solid understanding of foundational principles in machine learning. ● Master regression and classification methods for accurate data prediction and categorization in machine learning. ● Dive into advanced machine learning topics, including unsupervised learning and deep learning. DESCRIPTION The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in machine learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection, providing comprehensive coverage of various techniques such as the Fourier transform, short-time Fourier transform, and local binary patterns. Moving on, the book discusses principal component analysis and linear discriminant analysis. Next, the book covers the topics of model representation, training, testing, and cross-validation. It emphasizes regression and classification, explaining and implementing methods such as gradient descent. Essential classification techniques, including k-nearest neighbors, logistic regression, and naive Bayes, are also discussed in detail. The book then presents an overview of neural networks, including their biological background, the limitations of the perceptron, and the backpropagation model. It also covers support vector machines and kernel methods. Decision trees and ensemble models are also discussed. The final section of the book provides insight into unsupervised learning and deep learning, offering readers a comprehensive overview of these advanced topics. By the end of the book, you will be well-prepared to explore and apply machine learning in various real-world scenarios. WHAT YOU WILL LEARN ● Acquire skills to effectively prepare data for machine learning tasks. ● Learn how to implement learning algorithms from scratch. ● Harness the power of scikit-learn to efficiently implement common algorithms. ● Get familiar with various Feature Selection and Feature Extraction methods. ● Learn how to implement clustering algorithms. WHO THIS BOOK IS FOR This book is for both undergraduate and postgraduate Computer Science students as well as professionals looking to transition into the captivating realm of Machine Learning, assuming a foundational familiarity with Python. TABLE OF CONTENTS Section I: Fundamentals 1. An Introduction to Machine Learning 2. The Beginning: Data Pre-Processing 3. Feature Selection 4. Feature Extraction 5. Model Development Section II: Supervised Learning 6. Regression 7. K-Nearest Neighbors 8. Classification: Logistic Regression and Naïve Bayes Classifier 9. Neural Network I: The Perceptron 10. Neural Network II: The Multi-Layer Perceptron 11. Support Vector Machines 12. Decision Trees 13. An Introduction to Ensemble Learning Section III: Unsupervised Learning and Deep Learning 14. Clustering 15. Deep Learning Appendix 1: Glossary Appendix 2: Methods/Techniques Appendix 3: Important Metrics and Formulas Appendix 4: Visualization- Matplotlib Answers to Multiple Choice Questions Bibliography

Statistics For Machine Learning

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Author : Himanshu Singh
language : en
Publisher: BPB Publications
Release Date : 2021-01-15

Statistics For Machine Learning written by Himanshu Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-15 with Computers categories.

A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem Ê KEY FEATURESÊ _ Develop a Conceptual and Mathematical understanding of Statistics _ Get an overview of Statistical Applications in Python _ Learn how to perform Hypothesis testing in Statistics _ Understand why Statistics is important in Machine Learning _ Learn how to process data in Python Ê DESCRIPTIONÊÊ This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc.Ê You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning. Ê WHAT YOU WILLÊ LEARNÊÊ _ Understand the basics of Statistics _ Get to know more about Descriptive Statistics _ Understand and learn advanced Statistics techniques _ Learn how to apply Statistical concepts in Python _ Understand important Python packages for Statistics and Machine Learning Ê WHO THIS BOOK IS FORÊ This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite. TABLE OF CONTENTSÊ 1. Introduction to Statistics 2. Descriptive Statistics 3. Probability 4. Random Variables 5. Parameter Estimations 6. Hypothesis Testing 7. Analysis of Variance 8. Regression 9. Non Parametric Statistics 10. Data Analysis using Python 11. Introduction to Machine Learning

Understanding Machine Learning

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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19

Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Deep Learning With Pytorch

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Author : Luca Pietro Giovanni Antiga
language : en
Publisher: Simon and Schuster
Release Date : 2020-07-01

Deep Learning With Pytorch written by Luca Pietro Giovanni Antiga and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Mathematics For Machine Learning

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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23

Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.