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Author: Mel Kettle Publisher: BookPOD ISBN: 0648254135 Category : Business & Economics Languages : en Pages : 174
Book Description
Are you feeling exhausted and overwhelmed? Do you feel like you have no time for yourself? Are you wondering how to regain your energy and find joy? Being a leader today is hard. We are pulled in so many directions, with big responsibilities and many livelihoods reliant on us. It may surprise you that our first responsibility is to care for ourselves. To make choices that are right for us, instead of right for others. With blurred boundaries between work and life, it can be difficult to find time for this. We’ve glorified being busy to become over-scheduled and over-committed and feel guilty about taking time for ourselves. Fully Connected is for leaders who want to take back ownership of their lives and reclaim their health and energy. On their terms. When you figure out what lights you up and how to say no to what doesn’t bring you joy, you become a better leader as you energise your co-workers, communicate with conviction and create a culture of belonging. In these pages Mel Kettle shares practical, simple and actionable ideas for you to increase your self-awareness, understand what motivates you and prioritise self-care so you can become a fully connected leader.
Author: Mel Kettle Publisher: BookPOD ISBN: 0648254135 Category : Business & Economics Languages : en Pages : 174
Book Description
Are you feeling exhausted and overwhelmed? Do you feel like you have no time for yourself? Are you wondering how to regain your energy and find joy? Being a leader today is hard. We are pulled in so many directions, with big responsibilities and many livelihoods reliant on us. It may surprise you that our first responsibility is to care for ourselves. To make choices that are right for us, instead of right for others. With blurred boundaries between work and life, it can be difficult to find time for this. We’ve glorified being busy to become over-scheduled and over-committed and feel guilty about taking time for ourselves. Fully Connected is for leaders who want to take back ownership of their lives and reclaim their health and energy. On their terms. When you figure out what lights you up and how to say no to what doesn’t bring you joy, you become a better leader as you energise your co-workers, communicate with conviction and create a culture of belonging. In these pages Mel Kettle shares practical, simple and actionable ideas for you to increase your self-awareness, understand what motivates you and prioritise self-care so you can become a fully connected leader.
Author: Julia Hobsbawm Publisher: Bloomsbury Publishing ISBN: 1472926854 Category : Business & Economics Languages : en Pages : 361
Book Description
Shortlisted for the CMI's Management Book of the Year Award 2018 and the Business Book Awards 2018 Twenty-five years after the arrival of the Internet, we are drowning in data and deadlines. Humans and machines are in fully connected overdrive - and starting to become entwined as never before. Truly, it is an Age of Overload. We can never have imagined that absorbing so much information while trying to maintain a healthy balance in our personal and professional lives could feel so complex, dissatisfying and unproductive. Something is missing. That something, Julia Hobsbawm argues in this ground-breaking book, is Social Health, a new blueprint for modern connectedness. She begins with the premise that much of what we think about healthy ways to live have not been updated any more than have most post-war modern institutions, which are themselves also struggling in the twenty-first century. In 1946, the World Health Organization defined 'health' as 'a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.' What we understood by 'social' in the middle of the last century now desperately needs an update. In Fully Connected Julia Hobsbawm takes us on a journey – often a personal one, 'from Telex to Twitter' – to illustrate how the answer to the Age of Overload can come from devising management-based systems which are both highly practical and yet intuitive, and which draw inspiration from the huge advances the world has made in tackling other kinds of health, specifically nutrition, exercise, and mental well-being. Drawing on the latest thinking in health and behavioural economics, social psychology, neuroscience, management and social network analysis, this book provides a cornucopia of case studies and ideas, to educate and inspire a new generation of managers, policymakers and anyone wanting to navigate through the rough seas of overload.
Author: Bharath Ramsundar Publisher: "O'Reilly Media, Inc." ISBN: 1491980400 Category : Computers Languages : en Pages : 247
Book Description
Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units
Author: Tom Hope Publisher: "O'Reilly Media, Inc." ISBN: 1491978481 Category : Computers Languages : en Pages : 242
Book Description
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
Author: Paolo Galeone Publisher: Packt Publishing Ltd ISBN: 1789613795 Category : Computers Languages : en Pages : 346
Book Description
A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0 Key FeaturesUnderstand the basics of machine learning and discover the power of neural networks and deep learningExplore the structure of the TensorFlow framework and understand how to transition to TF 2.0Solve any deep learning problem by developing neural network-based solutions using TF 2.0Book Description TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production. What you will learnGrasp machine learning and neural network techniques to solve challenging tasksApply the new features of TF 2.0 to speed up developmentUse TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelinesPerform transfer learning and fine-tuning with TensorFlow HubDefine and train networks to solve object detection and semantic segmentation problemsTrain Generative Adversarial Networks (GANs) to generate images and data distributionsUse the SavedModel file format to put a model, or a generic computational graph, into productionWho this book is for If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful. Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.
Author: Maliheh Eftekhari Publisher: Frontiers Media SA ISBN: 2832549713 Category : Science Languages : en Pages : 246
Book Description
Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis
Author: Frank Millstein Publisher: Frank Millstein ISBN: Category : Computers Languages : en Pages : 643
Book Description
Programming With Python - 8 BOOK BUNDLE!! Deep Learning With Keras Here Is A Preview Of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models And much more... Convolutional Neural Networks In Python Here Is A Preview Of What You’ll Learn Here… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator How to build a simple image classification CNN And much, much more! Python Machine Learning Here Is A Preview Of What You’ll Learn Here… Basics behind machine learning techniques Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests Solving multi-clasisfication problems Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn Solving multi-label classification problems And much, much more... Machine Learning With TensorFlow Here Is A Preview Of What You’ll Learn Here… What is machine learning Main uses and benefits of machine learning How to get started with TensorFlow, installing and loading data Data flow graphs and basic TensorFlow expressions Creating MNIST classifiers with one-hot transformation And much, much more... Data Analytics With Python Here Is A Preview Of What You’ll Learn Here… What is Data Analytics? Difference between data science, big data and data analytics Installing python Python data structures Pandas series and data frames And much, much more... Natural Language Processing With Python Here Is A Preview Of What You’ll Learn Here… Challenges of natural language processing How natural language processing works? Part of speech tagging N-grams Running natural language processing script And much, much more... DevOps Handbook Here Is A Preview Of What You’ll Learn Here… Issues and mistakes plaguing software development What is software development life cycle? How software development life cycle works? The origins of devops Testing and building systems tools And much, much more... DevOps Adoption Here Is A Preview Of What You’ll Learn Here… Devops definition Overcoming traditional dev and ops Devops and security integration Devops success factors Is devops right for you? And much, much more... Get this book bundle NOW and SAVE money!
Author: Ronald T. Kneusel Publisher: No Starch Press ISBN: 1718500750 Category : Computers Languages : en Pages : 463
Book Description
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Author: Mel Kettle
Author: Mel Kettle
Author: Julia Hobsbawm
Author: Bharath Ramsundar
Author: Tom Hope
Author: Paolo Galeone
Author: Yaw Boadu-Ayeboafoh
Author: Maliheh Eftekhari
Author: Antonio Fernández-Caballero
Author: Frank Millstein
Author: Ronald T. Kneusel