If you are curious about the basics of artificial intelligence, blockchain technology, and quantum computing as key enablers for digital transformation and innovation, Digital Fluency is your handy guide. The real-world applications of these cutting-edge technologies are expanding rapidly, and your daily life will continue to be affected by each of them. There is no better time than now to get started and become digitally fluent.
You need not have previous knowledge of these versatile technologies, as author Volker Lang will expertly guide you through this digital age. He illustrates key concepts and applications in numerous practical examples and more than 48 catchy figures throughout Digital Fluency. The end of each chapter presents you with a helpful implementation checklist of central lessons before proceeding to the next. This book gets to the heart of digital buzzwords and concepts, and tells you what they truly mean.
Breaking down topics such as automated driving and intelligent robotics powered by artificial intelligence, blockchain-based cryptocurrencies and smart contracts, drug development and optimization of financial investment portfolios by quantum computing, and more is imperative to being ready for what the future of industry holds. Whether your own digital transformation journey takes place within your private or public organization, your studies, or your individual household, Digital Fluency maps out a concrete digital action plan for all of your technology and innovation strategy needs.
What You Will Learn
Gain guidance in the digital age without requiring any previous knowledge about digital technologies and digital transformation
Get acquainted with the most popular current and prospective applications of artificial intelligence, blockchain technology, and quantum computing across a wide range of industries including healthcare, financial services, and the automobile industry
Become familiar with the digital innovation models of Amazon, Google, Microsoft, IBM, and other world-leading organizations
Implement your own digital transformation successfully along the eight core dimensions of a concrete digital action plan
Who This Book Is For
Thought-leaders, business executives and industry strategists, management and strategy consultants, politicians and policy makers, entrepreneurs, financial analysts, investors and venture capitalists, students and research scientists, as well as general readers, who want to become digitally fluent.
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 90290.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
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.
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Mathematical Logic: Mathematics of Logic or Logic Of Mathematics? Zvonimir Sikic
The McCulloch-Pitts Paper from the Perspective of Mathematical Logic Tin Perkov
From the Linguistic Turn to the Cognitive Turn and Back Again Marina Novina
Why not Fuzzy Logic? Ivan Restovic
Meaning as Use: From Wittgenstein to Google's Word2Vec Ines Skelac and Andrej Jandric
Rudolf Carnap, the Grandfather of Artificial Neural Networks: The Influence of Carnap's Philosophy on Walter Pitts Marko Kardum
A Lost Croatian Cybernetic Machine Translation Program Sandro Skansi, Leo Mrsic, and Ines Skelac
The Architecture of Geoffrey Hinton Ivana Stanko
Machine Learning and the Philosophical Problems of Induction Davor Lauc
The Artificial Intelligence Singularity: What it Is and What it Is Not Borna Jalsenjak
AI-Completeness: Using Deep Learning to Eliminate the Human Factor Kristina Sekrst
Transhumanism and Artificial Intelligence: Philosophical Aspects Ivana Greguric Knezevic
Автор: Gupta N., Mangla R. Название: Artificial Intelligence Basics: A Self-Teaching Introduction ISBN: 1683925165 ISBN-13(EAN): 9781683925163 Издательство: Неизвестно Рейтинг: Цена: 44130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Designed as a self-teaching introduction to the fundamental concepts of artificial intelligence, this book begins with its history, the Turing test, and early applications. Later chapters cover the basics of searching, game playing, and knowledge representation. Expert systems and machine learning are also covered in detail.
Автор: Vishwas, B V, Patel, Ashish Название: Hands-On Time Series Analysis with Python: From Basics to Bleeding Edge Techniques ISBN: 1484259912 ISBN-13(EAN): 9781484259917 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.
You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow.
It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn:* Explains basics to advanced concepts of time series* How to design, develop, train, and validate time-series methodologies* What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results* Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.
* Univariate and multivariate problem solving using fbprophet. Who This Book Is ForData scientists, data analysts, financial analysts, and stock market researchers
Автор: Esra Bas Название: Basics of Probability and Stochastic Processes ISBN: 3030323226 ISBN-13(EAN): 9783030323226 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This textbook explores probability and stochastic processes at a level that does not require any prior knowledge except basic calculus. It presents the fundamental concepts in a step-by-step manner, and offers remarks and warnings for deeper insights. The chapters include basic examples, which are revisited as the new concepts are introduced. To aid learning, figures and diagrams are used to help readers grasp the concepts, and the solutions to the exercises and problems. Further, a table format is also used where relevant for better comparison of the ideas and formulae. The first part of the book introduces readers to the essentials of probability, including combinatorial analysis, conditional probability, and discrete and continuous random variable. The second part then covers fundamental stochastic processes, including point, counting, renewal and regenerative processes, the Poisson process, Markov chains, queuing models and reliability theory. Primarily intended for undergraduate engineering students, it is also useful for graduate-level students wanting to refresh their knowledge of the basics of probability and stochastic processes.
Interested in starting Data analysis and not sure which Programming language to use to develop Data Analysis Programs? Are you a beginner and would like to learn Python in order to develop the necessary skills for data analysis and note sure where to start?
Do you have data and want to analyze and provide professional figures for your presentation and communication and don't know which programming language to use or interested in using Python?
This book is intended to address all these questions. In fact, the aim of this book is providing the absolute beginners or other programmers that has no experience with Python programming the basic and fundamental tools of the Python language.
This book provides not only the basic syntax of Python but also an advanced command to evaluate the performance of your code in interactive programming environment (i.e. Jupyter) and a guide to use the fundamentals Python libraries for data analysis which are Pandas, NumPy and Matplotlib libraries. All these libraries are presented with examples with real data so you can practice at the same time when learning the functionalities of this library.
By reading this book, you will learn:
- The characteristics and utilities of using Python over other programming languages
- How to get started and install Python
- The basic syntax of Python programming and different basic data structures in Python
- How to develop modules and functions in Python
- How to install and use magic command in Ipython
- Functionalities of NumPy library for numerical programming
- Functionalities of Pandas library for data analysis
- Creating and customizing figures to visualize data with Matplotlib library
Even if don't have any background or skills in programming, this book will provide you and help you develop the required skills and basic technics to start data analysis with Python programming. It also provides you with code and data so you can practice. By reading this book will easily learn how to use python and Python libraries for easy data analysis and management. By developing the skills provided in this book, you will easily develop more advanced skills and acquire advanced technics that will allow to go from a beginner to an expert in Python.
We live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above. Being an introductory guide, expect to learn the various basics involved in Machine Learning and Python. This book provides an insight into the new world of big data, then behooves you to learn more about Machine Learning.
With a detailed and concise overview of the fundamentals, along with the challenges and limitations currently being tackled by the pros, inside this comprehensive guide you will
-Learn the Fundamentals of Machine Learning which Are Being Developed and Advanced with Python
-What is Machine Learning and how it is applied in real-world situations
-Algorithms, in a Language that Requires No Prior Background in Python
-Discover best practices for evaluating and tuning models
-Discover the Details of the Supervised, Unsupervised, and Reinforcement Algorithms, which Serve as the Skeleton of Hundreds of Machine Learning Algorithms Being Developed Every Day
-Become Familiar with Data Science Technology, an Umbrella Term Used for the Cutting-Edge Technologies of Today
-Understand the Entire Process of Creating Neural Network Models on TensorFlow, Using Open Source Data Sets and real Python Code
-Uncover the Secrets of the Most Critical Aspect of Developing a Machine Learning Model - Data Pre-Processing and Training/Testing Subsets
-Artificial Neural Networks
-And Much More
So what are you waiting for?
Even if some concepts of Machine Learning algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way.
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