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07 March, 2021

Introduction to Deep Learning and Neural Networks with Python: A Practical Guide

 

Introduction to Deep Learning and Neural Networks with Python: A Practical Guide

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by Ahmed Fawzy Gad, Fatima Ezzahra Jarmouni
  • Length: 300 pages
  • Edition: 1
  • Publisher: Academic Press
  • Publication Date: 2020-12-10
  • Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python™ code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and Python™ examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network.
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06 March, 2021

Excel 2019 for Advertising Statistics: A Guide to Solving Practical Problems

Excel 2019 for Advertising Statistics: A Guide to Solving Practical Problems

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Author(s): Thomas J. Quirk; Eric Rhiney

Publisher: Springer International Publishing, Year: 2020

Newly revised for Excel 2019, this text is a step-by-step guide for students taking a first course in statistics for advertising and for advertising managers and practitioners who want to learn how to use Excel to solve practical statistics problems in the workplace, whether or not they have taken a course in statistics.

Excel 2019 for Advertising Statistics explains statistical formulas and offers practical examples for how students can solve real-world advertising statistics problems. Each chapter offers a concise overview of a topic, and then demonstrates how to use Excel commands and formulas to solve specific advertising statistics problems. This book demonstrates how to use Excel 2019 in two different ways:  (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel’s drop-down formula menus (e.g., simple linear regression, multiple correlation and multiple regression, and one-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an appendix. An additional practice test allows readers to test their understanding of each chapter by attempting to solve a specific practical advertising statistics problem using Excel; the solution to each of these problems is also given in an appendix. This latest edition features a wealth of new end-of-chapter problems and an update of the chapter content throughout.

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05 March, 2021

Hands-On Quantum Information Processing with Python: Get Up and Running with Information Processing and Computing Based on Quantum Mechanics Using Python

 

Hands-On Quantum Information Processing with Python: Get Up and Running with Information Processing and Computing Based on Quantum Mechanics Using Python

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Author(s): Makhamisa Senekane

Publisher: Packt Publishing, Year: 2021

Description:

Explore the potential of quantum information processing and understand the state of a quantum system with this practical guide


Key Features:

  • Get well-versed with quantum information processing using Python
  • Understand the basics of quantum cryptography by implementing quantum key distribution protocols in Python
  • Implement well-known games such as the CHSH and GHZ games using quantum strategies and techniques


Book Description:

Quantum computation is the study of a subclass of computers that exploits the laws of quantum mechanics to perform certain operations that are thought to be difficult to perform on a non-quantum computer.


Hands-On Quantum Information Processing with Python begins by taking you through the essentials of quantum information processing to help you explore its potential. Next, you'll become well-versed with the fundamental property of quantum entanglement and find out how to illustrate this using the teleportation protocol. As you advance, you'll discover how quantum circuits and algorithms such as Simon's algorithm, Grover's algorithm, and Shor's algorithm work, and get to grips with quantum cryptography by implementing important quantum key distribution (QKD) protocols in Python. You will also learn how to implement non-local games such as the CHSH game and the GHZ game by using Python. Finally, you'll cover key quantum machine learning algorithms, and these implementations will give you full rein to really play with and fully understand more complicated ideas.


By the end of this quantum computing book, you will have gained a deeper understanding and appreciation of quantum information.


What You Will Learn:

Discover how quantum circuits and quantum algorithms work

Familiarize yourself with non-local games and learn how to implement them

Get to grips with various quantum computing models

Implement quantum cryptographic protocols such as BB84 and B92 in Python

Explore entanglement and teleportation in quantum systems

Find out how to measure and apply operations to qubits

Delve into quantum computing with the continuous-variable quantum state

Get acquainted with essential quantum machine learning algorithms


Who this book is for:

This book is for developers, programmers, or undergraduates in computer science who want to learn about the fundamentals of quantum information processing. A basic understanding of the Python programming language is required, and a good grasp of math and statistics will be useful to get the best out of this book.

04 March, 2021

Learn Python Programming For Beginners: The Complete Guide To Easily Get From Beginner To Advanced Level

Learn Python Programming For Beginners: The Complete Guide To Easily Get From Beginner To Advanced Level

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by LEWIS SMITH
  • Length: 131 pages
  • Edition: 1
  • Publication Date: 2021-02-21
  • Learn Python Programming for Beginners–The Ultimate and Complete Tutorial to Easily Get the Python Intermediate Level with Step-by-Step Practical Exercise, to Code with Python Starting from Scratch.

    Learning to code is essential to keep up with the times, increasing the opportunities that life has to offer you.

    Whether you are a tech enthusiast, enterprising student, or entrepreneur, if you choose to learn Python you are making the right and winning choice.

    Web development? Artificial intelligence? Automation and IoT? Python is all of this and more!

    Python can be used as an effective choice in any application and project, be it small or large. This characteristic makes it encountered in any modern software development scenario.

    Did you know that Python is one of the languages behind extremely popular services and websites like Instagram, YouTube, Reddit, and Mozilla?

    You cannot enter the magic and rich IT world without knowing what Python is and how it works…

    … and this incredibly exhaustive tutorial will give you all the knowledge and information you need to become a Python Pro!

    In this book, you will:

    • Clearly and Easily Understand What  Python Is and How It Works, starting from the instructions to correctly install it on your PC to show you how it runs and works.
    • Discover Secret Tips and Tricks to Get Started with Python for Beginners to enhance your skills and help you with daily data science tasks. If you want to make your Python coding more efficient, do not miss these tips/tricks!
    • Learn the Best Machine Learning Algorithms for Beginners with Coding Samples in Python; it is excellent for algorithmic design, as it is used extensively in data science and machine learning technologies.
    • Get the Fundamentals of Python Data Structures to introduce you to object-oriented design and data structures using this popular programming language, and give you the necessary knowledge to do whatever you want with Python.
    • Learn How Python Makes Decisions to Control Flow in Programming. It is crucial to control the program execution because, in real scenarios, the situations are full of conditions, and if you want your program to mimic the real world closer, then you need to transform those real-world situations into your program.
    • … & Lot More!

    For those new to programming, the number one priority is to sit in front of the screen and learn how to program as quickly as possible!

    Python was designed not only to be simple to understand but also fun to use. You can create prototypes and mini-programs very quickly, to immediately experience real satisfaction.

    It is thanks to this simplicity that it has gained not only a great deal of popularity but also a reputation as an “easy to learn language”.

    Python Programming for Beginners will become your best friend in helping you enter the Python world as smoothly as possible; all you need to know and the support is right here at your fingertips.

    You have only to click on the button below and…

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Hands on Data Science for Biologists Using Python 2021

Hands on Data Science for Biologists Using Python 2021

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by Rajkumar Chakraborty, Yasha Hasija
  • Length: 298 pages
  • Edition: 1
  • Publisher: CRC Press
  • Publication Date: 2021-04-09
  • Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language.

    The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples.

    Features of this book:

    • The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners.
    • This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases.
    • Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting.
    • Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems.

    Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.

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03 March, 2021

Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R 2021

Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R 2021

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by Robert P. Hirsch
  • Length: 640 pages
  • Edition: 2
  • Publisher: Wiley
  • Publication Date: 2021-02-17
  • The second edition of Introduction to Biostatistical Applications in Health Research delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.

    The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You’ll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.

    The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:

    Extensive discussions of basic and foundational concepts in statistical methods, including Bayes’ Theorem, populations, and samples A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables An examination of bivariable analysis, including regression analysis and correlation analysis An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.

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Introduction To Financial Modelling: How to Excel at Being a Lazy Modeller 2021

Introduction To Financial Modelling: How to Excel at Being a Lazy Modeller 2021

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by Liam Bastick
  • Length: 324 pages
  • Edition: I
  • Publisher: Holy Macro! Books
  • Publication Date: 2020-04-01
  • A simple walk-through of the common perils and pitfalls of financial modelling, this book examines the most common and necessary Excel functions, emphasizes the importance of a standardized and functional layout, explains accounting concepts simply, and reinforces four key concepts of best practice: consistency, robustness, flexibility, and transparency—CraFT. With more than fifty examples and an extended case study, this hands-on book helps users work with Excel more efficiently and effectively. This simple methodology has been adopted by many seasoned professionals who no longer must resort to balancing figures, circulars, and macros.
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Improve your skills with Google Sheets: Professional training 2021

Improve your skills with Google Sheets: Professional training 2021

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by Rémy Lentzner
  • Length: 132 pages
  • Edition: 1
  • Publisher: Remylent
  • Publication Date: 2020-07-23
  • Welcome to Google Sheets, your free worksheet on line. This book outlines useful possibilities if you want to work with this application. As with any spreadsheet, you can achieve simple or more complex calculations using functions. You will learn how to filter and sort out information creating statistics with Pivot tables. You will define page layout settings to print data easily. Charts will facilitate data analysis. Because Google Sheets is designed for sharing data, forms will enable you to create online questionnaires or surveys to get to know your contacts better. You will create macros that automate keyboard or mouse actions.
    This book will improve your knowledge about Google Sheets and how to use it in your work.
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02 March, 2021

Excel and the World Wide Web Straight to the Point 2021

 

Excel and the World Wide Web Straight to the Point 2021

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by Eduardo N Sanchez
  • Length: 85 pages
  • Edition: 1
  • Publisher: Holy Macro! Books
  • Publication Date: 2021-02-01
  • If you have an Excel workbook that needs to regularly harvest data from a web page, this book is for you. The book covers various methods for getting data from the web, from VBA to Selenium to Power Query. Addresses the complexities of getting data from the Modern Web and the lack of VBA support in MIcrosoft Edge.
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Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

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Author(s): Denis Rothman

Publisher: Packt Publishing Ltd, Year: 2021

Description:

Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models

Key Features
  • Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models
  • Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine
  • Learn training tips and alternative language understanding methods to illustrate important key concepts
Book Description

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.

The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.

The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.

By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.

What you will learn
  • Use the latest pretrained transformer models
  • Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
  • Create language understanding Python programs using concepts that outperform classical deep learning models
  • Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
  • Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
  • Measure the productivity of key transformers to define their scope, potential, and limits in production
Who this book is for

Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.

Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.

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01 March, 2021

Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras

Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras

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Author(s): Vaibhav Verdhan

Publisher: Apress, Year: 2021

Product Description

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. 

This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. 

What You'll Learn

  • Examine deep learning code and concepts to apply guiding principals to your own projects
  • Classify and evaluate various architectures to better understand your options in various use cases
  • Go behind the scenes of basic deep learning functions to find out how they work

Who This Book Is For

Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.

From the Back Cover

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. 

This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. 

You will:

  • Examine deep learning code and concepts to apply guiding principles to your own projects
  • Classify and evaluate various architectures to better understand your options in various use cases
  • Go behind the scenes of basic deep learning functions to find out how they work

About the Author

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist. 

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28 February, 2021

Hedge Fund Modeling and Analysis Using Excel and VBA

Hedge Fund Modeling and Analysis Using Excel and VBA

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by David Hampton, Paul Darbyshire
  • Length: 278 pages
  • Edition: 1
  • Publisher: Wiley
  • Co-authored by two respected authorities on hedge funds and asset management, this implementation-oriented guide shows you how to employ a range of the most commonly used analysis tools and techniques both in industry and academia, for understanding, identifying and managing risk as well as for quantifying return factors across several key investment strategies. The book is also suitable for use as a core textbook for specialised graduate level courses in hedge funds and alternative investments.

    The book provides hands-on coverage of the visual and theoretical methods for measuring and modelling hedge fund performance with an emphasis on risk-adjusted performance metrics and techniques. A range of sophisticated risk analysis models and risk management strategies are also described in detail. Throughout, coverage is supplemented with helpful skill building exercises and worked examples in Excel and VBA.

    The book’s dedicated website, www.darbyshirehampton.com provides Excel spreadsheets and VBA source code which can be freely downloaded and also features links to other relevant and useful resources.

    A comprehensive course in hedge fund modelling and analysis, this book arms you with the knowledge and tools required to effectively manage your risks and to optimise the return profile of your investment style.

    Table of Contents

    Chapter 1 The Hedge Fund Industry
    Chapter 2 Major Hedge Fund Strategies
    Chapter 3 Hedge Fund Data Sources
    Chapter 4 Statistical Analysis
    Chapter 5 Risk-Adjusted Return Metrics
    Chapter 6 Asset Pricing Models
    Chapter 7 Hedge Fund Market Risk Management

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