Manage Your Own Learning Analytics: Implement a Rasch Modelling Approach, McKay Elspeth
Автор: Pramod Singh; Avinash Manure Название: Learn TensorFlow 2.0 ISBN: 1484255607 ISBN-13(EAN): 9781484255605 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn TensorFlow 2.0 Chapter 1: TensorFlow 2.0 - An Introduction Chapter Goal: Introducing TensorFlow, major features, version 2.0 release. Chapter 2: Supervised Learning with TensorFlow 2.0Chapter Goal: Implementation of linear, logistic, SVM (Support Vector Machines) and random forest using TensorFlow. Chapter 3: Neural Networks and Deep Learning with TensorFlow 2.0Chapter Goal: Introduction to neural networks, deep learning and implementation using TensorFlow This chapter offers a detailed view of building Deep Learning models for various applications such as Forecasting using TensorFlow 2.0. The chapter also introduces optimization approaches and the techniques for hyper parameter tuning. Chapter 4: Images with TensorFlow 2.0Chapter Goal: TensorFlow 2.0 for images. This chapter focuses on building deep learning based models for image classification using TensorFlow 2.0. It covers advanced techniques such as GANs and transfer learning to image processing and classifications Chapter 5: Sequence to Sequence Modeling with TensorFlow 2.0 Chapter Goal: To understand sequence modeling using TensorFlow 2.0. This chapter covers the process of using different neural networks for NLP based tasks in TensorFlow 2.0. This includes sequence to sequence prediction, text translation using deep learning in TensorFlow 2.0 Chapter 6: TensorFlow 2.0 Models in Productionization Chapter Goal: Implementation of distributed training using TensorFlow. This chapter covers the process of scaling up the machine learning model training by implementing distributed training of TensorFlow models and deploying those models into production using TensorFlow serving layer
Автор: Ayyadevara, V Kishore Название: Pro machine learning algorithms ISBN: 1484235630 ISBN-13(EAN): 9781484235638 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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 modelsImplement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithmGain the tricks of ensemble learning to build more accurate modelsDiscover 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.
Автор: Kulkarni Название: Time Series Algorithms Recipes ISBN: 1484289773 ISBN-13(EAN): 9781484289778 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn * Implement various techniques in time series analysis using Python. * Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting * Understand univariate and multivariate modeling for time series forecasting * Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is For Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
Do you want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model, and you have no programming skills? Do you want to get started with learning data science?
This book is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This book is going to help you understand the various methods of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
Machine learning implies programming. This book will teach you Python programming. This book does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning.
Overall, this book is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the book, you will be able to develop your machine learning models using Python.
Through this book, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to build and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models.
Автор: Parisi, Alessandro Название: Hands-on artificial intelligence for cybersecurity ISBN: 1789804027 ISBN-13(EAN): 9781789804027 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: If you wish to design smart, threat-proof cybersecurity systems using trending AI tools and techniques, then this book is for you. With this book, you will learn to develop intelligent systems that can detect suspicious patterns and attacks, thereby allowing you to protect your network and corporate assets.
Автор: Mitchell Laura, K Sri Yogesh, Subramanian Vishnu Название: Deep Learning with PyTorch 1.x - Second Edition ISBN: 1838553002 ISBN-13(EAN): 9781838553005 Издательство: Неизвестно Рейтинг: Цена: 5516.00 р. Наличие на складе: Нет в наличии.
Описание: With practical examples, this book teaches you how to effectively implement deep learning techniques to build neural network architectures. This book will be useful for anyone who wants to implement deep learning concepts using the latest version of PyTorch
Описание: Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics: 1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3: Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics: 1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4: Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics: 1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised L
Автор: Jadon Shruti, Garg Ankush Название: Hands-On One-shot Learning with Python ISBN: 1838825460 ISBN-13(EAN): 9781838825461 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a step by step guide to one-shot learning using Python-based libraries. It is designed to help you understand and design models that can learn information about your data from one, or only a few, training examples. You will also learn to apply these techniques with real-world examples and datasets for classification and regression.
Do you want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model, and you have no programming skills? Do you want to get started with learning data science?
This book is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This book is going to help you understand the various methods of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
Machine learning implies programming. This book will teach you Python programming. This book does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning.
Overall, this book is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the book, you will be able to develop your machine learning models using Python.
Through this book, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to build and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models.
Автор: Andrew Burgess Название: The Executive Guide to Artificial Intelligence ISBN: 3319876457 ISBN-13(EAN): 9783319876450 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book takes a pragmatic and hype–free approach to explaining artificial intelligence and how it can be utilised by businesses today. At the core of the book is a framework, developed by the author, which describes in non–technical language the eight core capabilities of Artificial Intelligence (AI). Each of these capabilities, ranging from image recognition, through natural language processing, to prediction, is explained using real–life examples and how they can be applied in a business environment. It will include interviews with executives who have successfully implemented AI as well as CEOs from AI vendors and consultancies.
AI is one of the most talked about technologies in business today. It has the ability to deliver step–change benefits to organisations and enables forward–thinking CEOs to rethink their business models or create completely new businesses. But most of the real value of AI is hidden behind marketing hyperbole, confusing terminology, inflated expectations and dire warnings of ‘robot overlords’. Any business executive that wants to know how to exploit AI in their business today is left confused and frustrated.
As an advisor in Artificial Intelligence, Andrew Burgess regularly comes face–to–face with business executives who are struggling to cut through the hype that surrounds AI. The knowledge and experience he has gained in advising them, as well as working as a strategic advisor to AI vendors and consultancies, has provided him with the skills to help business executives understand what AI is and how they can exploit its many benefits. Through the distilled knowledge included in this book business leaders will be able to take full advantage of this most disruptive of technologies and create substantial competitive advantage for their companies.
Автор: Ankan Ankur, Panda Abinash Название: Hands-On Markov Models with Python ISBN: 1788625447 ISBN-13(EAN): 9781788625449 Издательство: Неизвестно Рейтинг: Цена: 7171.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.
Do you want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model, and you have no programming skills? Do you want to get started with learning data science?
This book is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This book is going to help you understand the various methods of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
Machine learning implies programming. This book will teach you Python programming. This book does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning.
Overall, this book is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the book, you will be able to develop your machine learning models using Python.
Through this book, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to build and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models.
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