Biznes IT

Deep Learning with Keras. Implementing deep learning models and neural networks with the power of Python

Antonio Gulli, Sujit Pal

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided.Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer.Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide. A practical guide to building neural networks using Microsoft's open source deep learning framework

Willem Meints

Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks.This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide. A practical guide to building neural networks using Microsoft's open source deep learning framework

Willem Meints

Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks.This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment

Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet

Andrés P. Torres, Paul Newman

Explore the capabilities of the open-source deep learning framework MXNet to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in Computer Vision, natural language processing, and more. The Deep Learning with MXNet Cookbook is your gateway to constructing fast and scalable deep learning solutions using Apache MXNet.Starting with the different versions of MXNet, this book helps you choose the optimal version for your use and install your library. You’ll work with MXNet/Gluon libraries to solve classification and regression problems and gain insights into their inner workings. Venturing further, you’ll use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. From building and training deep-learning neural network architectures from scratch to delving into advanced concepts such as transfer learning, this book covers it all. You'll master the construction and deployment of neural network architectures, including CNN, RNN, LSTMs, and Transformers, and integrate these models into your applications.By the end of this deep learning book, you’ll wield the MXNet and Gluon libraries to expertly create and train deep learning networks using GPUs and deploy them in different environments.

Deep Learning with PyTorch. A practical approach to building neural network models using PyTorch

Vishnu Subramanian

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.

Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python

Kunal Sawarkar, Dheeraj Arremsetty

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming.Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation.Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide.In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.By the end of this book, you’ll be able to build and deploy DL models with confidence.

Deep Learning with PyTorch Quick Start Guide. Learn to train and deploy neural network models in Python

David Julian

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

Deep Learning with PyTorch Quick Start Guide. Learn to train and deploy neural network models in Python

David Julian

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x

Swarna Gupta, Rehan Ali Ansari, Dipayan Sarkar

Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques.The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps.By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.

Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden)...

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.

Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition

Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

Deep Learning with TensorFlow and Keras - 3rd edition. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition

Dr. Amita Kapoor, Antonio Gulli, Sujit Pal

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.

Deep Learning with TensorFlow. Explore neural networks and build intelligent systems with Python - Second Edition

Giancarlo Zaccone, Vihan Jain, Md. Rezaul Karim,...

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

Deep Learning with TensorFlow. Explore neural networks with Python

Giancarlo Zaccone, Fabrizio Milo, Md. Rezaul Karim

Deep learning is the step that comes after machine learning, and has more advancedimplementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, includingsearch, image recognition, and language processing. Additionally, you’ll learn howto analyze and improve the performance of deep learning models. This can be done bycomparing algorithms against benchmarks, along with machine intelligence, to learnfrom the information and determine ideal behaviors within a specific context.After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

Deep Learning with Theano. Perform large-scale numerical and scientific computations efficiently

Christopher Bourez

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU.The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy.The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.

Deep learning z TensorFlow 2 i Keras dla zaawansowanych. Sieci GAN i VAE, deep RL, uczenie nienadzorowane, wykrywanie i segmentacja obiektów i nie tylko. Wydanie II

Rowel Atienza

Oto propozycja dla specjalistów zajmujących się programowaniem sztucznej inteligencji i studentów kształcących się w tej dziedzinie. Autor przybliża tajniki tworzenia sieci neuronowych stosowanych w uczeniu głębokim i pokazuje, w jaki sposób używać w tym celu bibliotek Keras i TensorFlow. Objaśnia zagadnienia dotyczące programowania AI zarówno w teorii, jak i praktyce. Liczne przykłady, czytelna oprawa graficzna i logiczne wywody sprawiają, że to skuteczne narzędzie dla każdego, kto chce się nauczyć budowania sieci neuronowych typu MLP, CNN i RNN. Książka wprowadza w teoretyczne fundamenty uczenia głębokiego - znalazły się w niej wyjaśnienia podstawowych pojęć związanych z tą dziedziną i różnice pomiędzy poszczególnymi typami sieci neuronowych. Opisano tutaj również metody programowania algorytmów używanych w uczeniu głębokim i sposoby ich wdrażania. Dzięki lekturze lepiej zrozumiesz sieci neuronowe, nauczysz się ich tworzenia i zastosowania w różnych projektach z zakresu AI. Polecamy tę książkę każdemu, kto: chce zrozumieć, jak działają sieci neuronowe i w jaki sposób się je tworzy specjalizuje się w uczeniu głębokim lub zamierza lepiej poznać tę dziedzinę posługuje się sieciami neuronowymi w programowaniu chce się nauczyć stosować biblioteki Keras i TensorFlow w uczeniu głębokim

Deep Reinforcement Learning Hands-On. Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Oleg Vasilev, Maxim Lapan, Martijn van Otterlo,...

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

Democratizing Application Development with Betty Blocks. Build powerful applications that impact business immediately with no-code app development

Reinier van Altena

This practical guide on no-code development with Betty Blocks will take you through the different features, no-code functionalities, and capabilities of the Betty Blocks platform using real-world use cases. The book will equip you with the tools to develop business apps based on various data models, business processes, and more.You’ll begin with an introduction to the basic concepts of the Betty Blocks no-code platform, such as developing IT solutions on various use cases including reporting apps, data tracking apps, workflows, and business processes. After getting to grips with the basics, you’ll explore advanced concepts such as building powerful applications that impact the business straight away with no-code application development and quickly creating prototypes. The concluding chapters will help you get a solid understanding of rapid application development, building customer portals, building dynamic web apps, drag-and-drop front ends, visual modelling capabilities, and complex data models.By the end of this book, you’ll have gained a comprehensive understanding of building your own applications as a citizen developer using the Betty Blocks no-code platform.

Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance

Fanny Ip, Jeremiah Crowley

Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems.Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You’ll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You’ll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid.By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.

De-Mystifying Math and Stats for Machine Learning. Mastering the Fundamentals of Mathematics and Statistics for Machine Learning

Govindakumar Madhavan

Beginning with basic concepts like central tendency, dispersion, and types of distribution, this course will help you build a robust understanding of data analysis. It progresses to more advanced topics, including hypothesis testing, outliers, and the intricacies of dependent versus independent variables, ensuring you grasp the statistical tools necessary for data-driven decision-making.Moving ahead, you'll explore the mathematical frameworks crucial for machine learning algorithms. Learn about the significance of percentiles, the distinction between population and sample, and the vital role of precision versus accuracy in data science. Chapters on linear algebra and regression will enhance your ability to implement and interpret complex models, while practical lessons on measuring algorithm accuracy and understanding key machine learning concepts will round out your expertise.The course culminates with an in-depth look at specific machine learning techniques such as decision trees, k-nearest neighbors (kNN), and gradient descent. Each chapter builds on the last, guiding you through a logical progression of knowledge and skills. By the end, you will have not only mastered the theoretical aspects but also gained practical insights into applying these techniques in real-world scenarios.

Design Thinking. Jak wykorzystać myślenie projektowe do zwiększenia zysków Twojej firmy

Danuta Piasecka

Zweryfikuj swoje pomysły biznesowe. Szybko i skutecznie! Design thinking, czyli myślenie projektowe. Wbrew pozorom ten sposób podejścia do biznesu nie jest przypisany wyłącznie do wielkich korporacji. Pracować w duchu design thinking może i powinna każda firma, której właścicielowi zależy na zwiększeniu wydajności, szybkim realizowaniu dobrych pomysłów i jeszcze szybszym wycofywaniu się z tych nietrafionych. Jeżeli zatem myślisz o tym, by w swojej - małej lub większej - firmie działać szybciej, a tym samym skuteczniej i z większym zyskiem, ta kursoksiążka jest dla Ciebie. Dlaczego kursoksiążka? Ponieważ jest to pozycja, w której informacje teoretyczne dotyczące samego pojęcia myślenia projektowego ograniczono do minimum. Autorka, bizneswoman, trenerka i moderatorka DT, stawia na wiedzę praktyczną i umiejętność myślenia projektowego, a także dostarczenie Ci narzędzi gotowych do zastosowania od zaraz. Za pośrednictwem kursoksiążki krok po kroku przeprowadzi Cię przez proces projektowania nowych produktów i usług, dzięki którym zwiększysz zyski Twojej firmy.

Design Thinking. Jak wykorzystać myślenie projektowe do zwiększenia zysków Twojej firmy

Danuta Piasecka

Zweryfikuj swoje pomysły biznesowe. Szybko i skutecznie! Design thinking, czyli myślenie projektowe. Wbrew pozorom ten sposób podejścia do biznesu nie jest przypisany wyłącznie do wielkich korporacji. Pracować w duchu design thinking może i powinna każda firma, której właścicielowi zależy na zwiększeniu wydajności, szybkim realizowaniu dobrych pomysłów i jeszcze szybszym wycofywaniu się z tych nietrafionych. Jeżeli zatem myślisz o tym, by w swojej - małej lub większej - firmie działać szybciej, a tym samym skuteczniej i z większym zyskiem, ta kursoksiążka jest dla Ciebie. Dlaczego kursoksiążka? Ponieważ jest to pozycja, w której informacje teoretyczne dotyczące samego pojęcia myślenia projektowego ograniczono do minimum. Autorka, bizneswoman, trenerka i moderatorka DT, stawia na wiedzę praktyczną i umiejętność myślenia projektowego, a także dostarczenie Ci narzędzi gotowych do zastosowania od zaraz. Za pośrednictwem kursoksiążki krok po kroku przeprowadzi Cię przez proces projektowania nowych produktów i usług, dzięki którym zwiększysz zyski Twojej firmy.

Design Thinking. Jak wykorzystać myślenie projektowe do zwiększenia zysków Twojej firmy

Danuta Piasecka

Zweryfikuj swoje pomysły biznesowe. Szybko i skutecznie! Design thinking, czyli myślenie projektowe. Wbrew pozorom ten sposób podejścia do biznesu nie jest przypisany wyłącznie do wielkich korporacji. Pracować w duchu design thinking może i powinna każda firma, której właścicielowi zależy na zwiększeniu wydajności, szybkim realizowaniu dobrych pomysłów i jeszcze szybszym wycofywaniu się z tych nietrafionych. Jeżeli zatem myślisz o tym, by w swojej - małej lub większej - firmie działać szybciej, a tym samym skuteczniej i z większym zyskiem, ta kursoksiążka jest dla Ciebie. Dlaczego kursoksiążka? Ponieważ jest to pozycja, w której informacje teoretyczne dotyczące samego pojęcia myślenia projektowego ograniczono do minimum. Autorka, bizneswoman, trenerka i moderatorka DT, stawia na wiedzę praktyczną i umiejętność myślenia projektowego, a także dostarczenie Ci narzędzi gotowych do zastosowania od zaraz. Za pośrednictwem kursoksiążki krok po kroku przeprowadzi Cię przez proces projektowania nowych produktów i usług, dzięki którym zwiększysz zyski Twojej firmy.

Designing the Human Business. Leveraging Design Thinking to craft powerful and innovative business models

Anthony Mills

Globally, 275,000 new business ventures get launched every single day, and ninety percent of them fail. One of the most fundamental reasons for that is that they don’t solve a real market problem that a real market population has, in a way that resonates with that market and sells their solution. Consequently, they struggle to gain traction and attain scale.In this book, you’ll learn what business models are. Additionally, you’ll find out what business model innovation is and, ultimately, how to use Design Thinking to identify not just a winning value proposition but also bring that value proposition to the market in a way that resonates with customers. In doing so, you’ll be able to unlock maximum value for your business, allowing it to attain maximum scale through growing waves of adopters.By the end of this book, you’ll understand what you need to do to uncover your target markets’ ‘reason to buy’, as well as how to wrap a winning business model around that reason so that your business can gain traction and achieve scale.

DevOps Adoption Strategies: Principles, Processes, Tools, and Trends. Embracing DevOps through effective culture, people, and processes

Martyn Coupland

DevOps is a set of best practices enabling operations and development teams to work together to produce higher-quality work and, among other things, quicker releases. This book helps you to understand the fundamentals needed to get started with DevOps, and prepares you to start deploying technical tools confidently.You will start by learning the key steps for implementing successful DevOps transformations. The book will help you to understand how aspects of culture, people, and process are all connected, and that without any one of these elements DevOps is unlikely to be successful. As you make progress, you will discover how to measure and quantify the success of DevOps in your organization, along with exploring the pros and cons of the main tooling involved in DevOps. In the concluding chapters, you will learn about the latest trends in DevOps and find out how the tooling changes when you work with these specialties.By the end of this DevOps book, you will have gained a clear understanding of the connection between culture, people, and processes within DevOps, and learned why all three are critically important.

DevOps Paradox. The truth about DevOps by the people on the front line

Viktor Farcic

DevOps promises to break down silos, uniting organizations to deliver high quality output in a cross-functional way. In reality it often results in confusion and new silos: pockets of DevOps practitioners fight the status quo, senior decision-makers demand DevOps paint jobs without committing to true change. Even a clear definition of what DevOps is remains elusive.In DevOps Paradox, top DevOps consultants, industry leaders, and founders reveal their own approaches to all aspects of DevOps implementation and operation. Surround yourself with expert DevOps advisors. Viktor Farcic draws on experts from across the industry to discuss how to introduce DevOps to chaotic organizations, align incentives between teams, and make use of the latest tools and techniques.With each expert offering their own opinions on what DevOps is and how to make it work, you will be able to form your own informed view of the importance and value of DevOps as we enter a new decade. If you want to see how real DevOps experts address the challenges and resolve the paradoxes, this book is for you.

Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems

Guanhua Wang

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.

Doktryna jakości. Rzecz o skutecznym zarządzaniu

Andrzej Jacek Blikle

"Doktryna jakości" na podium My Company Polska Choice!   Książka prof. Andrzeja Bliklego "Doktryna jakości. Rzecz o skutecznym zarządzaniu" zdobyła II miejsce w kategorii "Vademecum przedsiębiorcy" w konkursie My Company Polska Choice.   Wyróżnienie dla "Doktryna jakości. Rzecz o skutecznym zarządzaniu" podczas 19. Poznańskich Dni Książki nie tylko Naukowej. ECONOMICUS 2015 - I miejsce w kategorii "Najlepszy poradnik ekonomiczny"   Książka dostępna również w twardej oprawie » Nowa filozofia zarządzania Moja książka jest efektem doświadczeń, jakie nabywałem w latach 1990 – 2010, wdrażając zarządzanie jakością (TQM) w mojej rodzinnej firmie, a także ucząc tej metody w innych firmach i na uczelniach wyższych, co zresztą czynię do dziś. Pierwsze wykłady z TQM prowadziłem dla pracowników firmy A.Blikle, a notatki do tych wykładów stanowiły zaczątki Doktryny jakości. Początki oczywiście nie były łatwe. Wiedza konieczna do zarządzania zgodnie z TQM nie jest technicznie trudna, ale jej zaakceptowanie bywa trudne emocjonalnie. Bywa trudne, bo zaraz na samym początku trzeba porzucić wiele przekonań, do których jesteśmy przyzwyczajani niemal od dziecka: że nagrody motywują, że współzawodnictwo jest twórcze, że struktura zarządcza firmy musi być hierarchiczna… Nie było więc łatwo, ale było nadzwyczaj ciekawie, bo czasy pionierskie z zasady charakteryzują się wysokim poziomem zaangażowania. Pamiętam, jak nasz pierwszy nauczyciel TQM, Jim Murray, wyraził zdumienie, że moi pracownicy zgodzili się poświęcić na szkolenie dni wolne od pracy. „W Anglii to by nie było możliwe” - powiedział. Oczywiście na naukach Jima się nie skończyło. Dzięki niemu zacząłem jeździć do Anglii na coroczne konferencje Brytyjskiego Towarzystwa im. Deminga (twórcy TQM), a zdobywaną tam (również za pośrednictwem wielu znakomitych książek) wiedzę niezwłocznie przekazywałem moim pracownikom prowadząc dla nich wykłady. Materiały do książki rosły, a na wykłady zaczęli przychodzić słuchacze spoza firmy. I tak narodziło się moje konwersatorium z TQM, które prowadzę od roku 1997 raz w miesiącu od października do czerwca. Wstęp wolny, a informacje o programie można znaleźć na mojej witrynie www.moznainaczej.com.pl. To taka moja prywatna misja, gdyż uważam, że TQM może być wielką szansą dla polskiej gospodarki.   Patroni wydania: