Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
In this profound and yet accessible book, John Kekes discusses moral wisdom: a virtue essential to living a morally good and personally satisfying life. He advances a broad, nontechnical argument that considers the adversities inherent in the human condition and assists in the achievement of good lives. The possession of moral wisdom, Kekes asserts, is a matter of degree: more of it makes lives better, less makes them worse. Exactly what is moral wisdom, however, and how should it be sought? Ancient Greek and medieval Christian philosophers were centrally concerned with it. By contrast, modern Western sensibility doubts the existence of a moral order in reality; and because we doubt it, and have developed no alternatives, we have grown dubious about the traditional idea of wisdom. Kekes returns to the classical Greek sources of Western philosophy to argue for the contemporary significance of moral wisdom. He develops a proposal that is eudaimonistic—secular, anthropocentric, pluralistic, individualistic, and agonistic. He understands moral wisdom as focusing on the human effort to create many different forms of good lives. Although the approach is Aristotelian, the author concentrates on formulating and defending a contemporary moral ideal. The importance of this ideal, he shows, lies in increasing our ability to cope with life's adversities by improving our judgment. In chapters on moral imagination, self-knowledge, and moral depth, Kekes calls attention to aspects of our inner life that have been neglected because of our cultural inattention to moral wisdom. He discusses these inner processes through the tragedies of Sophocles, which can inspire us with their enduring moral significance and help us to understand the importance of moral wisdom to living a good life.
Kekes argues further that the liberal conceptions of equality, justice, and pluralism require treating good and evil people with equal respect, distributing resources without regard to what recipients deserve, and restricting choices to those that conform to liberal preconceptions. All these policies are detrimental to good lives. Kekes concludes that liberalism cannot cope with the prevalence of evil, that it is vitiated by inconsistent commitments, and that - contrary to its aim - liberalism is an obstacle to good lives.
John Parrington argues that social interaction and culture have deeply shaped the exceptional nature of human consciousness. The mental capacities of the human mind far outstrip those of other animals. Our imaginations and creativity have produced art, music, and literature; built bridges and cathedrals; enabled us to probe distant galaxies, and to ponder the meaning of our existence. When our minds become disordered, they can also take us to the depths of despair. What makes the human brain unique, and able to generate such a rich mental life? In this book, John Parrington draws on the latest research on the human brain to show how it differs strikingly from those of other animals in its structure and function at a molecular and cellular level. And he argues that this 'shift', enlarging the brain, giving it greater flexibility and enabling higher functions such as imagination, was driven by tool use, but especially by the development of one remarkable tool - language. The complex social interaction brought by language opened up the possibility of shared conceptual worlds, enriched with rhythmic sounds, and images that could be drawn on cave walls. This transformation enabled modern humans to leap rapidly beyond all other species, and generated an exceptional human consciousness, a sense of self that arises as a product of our brain biology and the social interactions we experience. Our minds, even those of identical twins, are unique because they are the result of this extraordinarily plastic brain, exquisitely shaped and tuned by the social and cultural environment in which we grew up and to which we continue to respond through life. Linking early work by the Russian psychologist Lev Vygotsky to the findings of modern neuroscience, Parrington explores how language, culture, and society mediate brain function, and what this view of the human mind may bring to our understanding and treatment of mental illness.
Since their inception, the presidential debates Americans have watched on television were carefully negotiated. This book closely examines a previously unexamined type of political communication, presidential debate negotiation. While it has been widely known that all general election presidential debates since 1960 have been negotiated by the participating candidates, no one has ever completed a systematic study of them. In particular, the 1960, 1976, 1980, 1984, and 1988 negotiations were examined in detail. For each of these election cycles, a comprehensive narrative of what occurred during the pre-debate negotiations was constructed based on primary source materials, media accounts, and other secondary sources. Comparisons across election cycles were made in order to draw some conclusions about presidential debate negotiation. Presidential debate negotiations are not just negotiations, but rather also a form of political rhetoric for several different players. The research concluded that that there are five contexts in which presidential debate rhetoric occurs and the rhetoric is aimed at two audiences. Within each context, the functions and strategies of the rhetoric were discussed and explained from the perspective of the sponsor of the debate(s), the candidates and their representatives, as well as the media.
This book is a discussion of key documents that explain the development, current status, and relevance of the international law governing the initiation of military hostilities. International Law and the Use of Force: A Documentary and Reference Guide brings to life a crucial body of law, explaining its historical origins, the core rules and principles of the regime embodied in the Charter of the United Nations, and contentious aspects of that law in the contemporary world. In light of the intensified interest in the question of justified or unjustified use of force, this timely resource introduces and analyzes over 40 documents relating to the legality of the initiation of military hostilities. The volume presents competing assessments of the legality of key uses of force and explains mainstream positions on important issues such as national right to self-defense, anticipatory and preemptive self-defense, terrorism, aggression, and the role of the UN Security Council. The book concludes by assessing whether the international law that seeks to limit the number of wars has in fact made the world a more peaceful place.
Here is the first practical guide for dealing with the moral issues that regularly confront clinicians in their work. Written for all mental health professionals, Doing the Right Thing: An Approach to Moral Issues in Mental Health Treatment offers a framework both for making moral decisions concerning the treatment of patients and for helping patients deal with their own moral concerns. Drawing on current thinking in several disciplines, Doing the Right Thing introduces the concept of moral functioning as a basis for therapeutic influence. Numerous case examples illustrate how to Assess patients' ability to function morally -- Learn how six basic capacities needed for moral functioning develop, and how identifying problems in an individual's moral functioning can help guide the formulation of a treatment plan. Treat patients with problems functioning morally -- Appreciate when it is time to set aside neutrality as a therapeutic stance in favor of a more direct approach to helping patients make moral commitments, decisions and self-assessments and develop moral character. Deal with the moral aspects of clinical decision-making -- Develop a framework for making moral choices in planning the direction of treatment, confronting resistance and addressing problems in caring effectively. Help patients address moral challenges -- Learn how to take into account your own and the patient's values in reasoning through moral dilemmas. Understand more clearly how to help patients deal with unfair pain caused by others, as well as the guilt and shame caused by their own moral failures. Employ the therapeutic potential of moral growth, transformation, and integration -- Discover the role of a clinician in helping demoralized patients reformulate their ideals for better outcomes. Recognize where a moral paradigm is useful in improving the delivery of mental health care. Concise, clear, and clinically relevant, Doing the Right Thing is a valuable, thought-provoking guide for both new and seasoned mental health practitioners who live and work in a morally complex environment. It is also an excellent supplementary text for courses dealing with the practice of psychotherapy and the ethical aspects of mental health care.
What a gift to education! By practicing the ideas in this book, school counselors everywhere can help create new descriptions and stories that will transform the academic lives and behaviors of their students." —Linda Metcalf, Author Counseling Toward Solutions and Solution–Focused School Counseling Promote students′ respect for themselves and others through narrative interventions! Narrative counseling is based on the premise that stories, rather than hard-nosed realities, shape our lives. By changing the stories that negatively label and define students, we help them open up new avenues and opportunities. In this second edition of their best-selling book, John Winslade and Gerald Monk present even more case studies, guidance, and examples of counseling practice to help students narrate stories that "redescribe" who they are and can be. Mindful that today′s busy counselors need effective and brief techniques, the authors make plain the steps with which counselors can externalize problems and draw out student self-knowledge to inform new ways of identifying and behaving. Updated throughout, this new edition offers: An exploration of ethically sound accountability practices Potential obstacles and suggestions for overcoming them Guidance to help students set goals Applications of narrative ideas to restorative justice An expanded section on group work, specifically focusing on anger management and grief counseling Grounded in a deep respect for students, this book′s principles and practices will enable students to choose for themselves the new reputations by which they′ll be known.
Just about everyone likes to listen to music to put them "in the mood," and these techniques get you "out" of a mood! The "Tao" part is about accepting what you're feeling, and dealing with it, by using Dr. Ortiz's methods. Includes musical menus that you can use to create your own program for dealing with issues, koans for meditation, and various other fun exercises to make music a part of your holistic health program. Appendix, bibliography, index.
Ireland's Aviator Heroes of World War II' celebrates Irish aviators who served in the Royal Air Force during the dark days of the Second World War. Individual stories have been painstakingly and extensively researched through interviews with the surviving subjects and their families, and using the original log books and service records of the men. Many of the stories contain details of the individuals' participation in important wartime campaigns, although others are limited to war service records due to a lack of surviving information. Above all this book celebrates the great courage and sacrifice demonstrated by these Irishmen during their contribution to the Allied war effort against Nazi Germany, her western allies and Japan. Many of the men included paid the ultimate price for their participation. The book is also well illustrated with many photographs, some taken by the subjects themselves.
John Barton's revised classic text is intended for students who have already learned some of the techniques of biblical study and who wish to explore the implications and aims of the various critical methods currently in use. Chapters include: form criticism, redaction criticism, canonical criticism, structuralism, reader-response criticism, and postmodern approaches.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
This perceptive book analyzes the scope of the duty to prevent genocide of China, France, Russia, the UK, and the US in light of the due diligence standard under conventional, customary, and peremptory international law. It expounds the positive obligations of these five states to act both within and without the Security Council context to prevent or suppress an imminent or ongoing genocide.
To Danny, Joel (fearless of sharks) and Steve of Daytona Beach, and Raul of Long Beach, surfing is first and foremost. The surfers lives unfold in adventure as well as misadventure and romance. Surfer girls follow the young surfers from beach to beach. Some of them find marriage relationships along the way. These surfers' adventures involve Captain Mac's boat and Raul's airplane as well as surfing. The surfers experience driving through hurricanes after the police are gone from the highways, plane crashes, title waves and boat wrecks and surfing in the wake of storms. They chase the storm's wake to surf the killer surf. They surf around the world and the story climaxes in Palm Beach after Hurricane Floyd moves off the South Florida Coast.
John Dewey's Experience and Nature has been considered the fullest expression of his mature philosophy since its eagerly awaited publication in 1925. Irwin Edman wrote at that time that "with monumental care, detail and completeness, Professor Dewey has in this volume revealed the metaphysical heart that beats its unvarying alert tempo through all his writings, whatever their explicit themes." In his introduction to this volume, Sidney Hook points out that "Dewey's Experience and Nature is both the most suggestive and most difficult of his writings." The meticulously edited text published here as the first volume in the series The Later Works of John Dewey, 1925-1953 spans that entire period in Dewey's thought by including two important and previously unpublished documents from the book's history: Dewey's unfinished new introduction written between 1947 and 1949, edited by the late Joseph Ratner, and Dewey's unedited final draft of that introduction written the year before his death. In the intervening years Dewey realized the impossibility of making his use of the word 'experience' understood. He wrote in his 1951 draft for a new introduction: "Were I to write (or rewrite) Experience and Nature today I would entitle the book Culture and Nature and the treatment of specific subject-matters would be correspondingly modified. I would abandon the term 'experience' because of my growing realization that the historical obstacles which prevented understanding of my use of 'experience' are, for all practical purposes, insurmountable. I would substitute the term 'culture' because with its meanings as now firmly established it can fully and freely carry my philosophy of experience.
The new edition of an introduction to the art of computational problem solving using Python. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data as well as substantial material on machine learning. All of the code in the book and an errata sheet are available on the book’s web page on the MIT Press website.
Leverage benefits of machine learning techniques using Python About This Book Improve and optimise machine learning systems using effective strategies. Develop a strategy to deal with a large amount of data. Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn Learn to write clean and elegant Python code that will optimize the strength of your algorithms Uncover hidden patterns and structures in data with clustering Improve accuracy and consistency of results using powerful feature engineering techniques Gain practical and theoretical understanding of cutting-edge deep learning algorithms Solve unique tasks by building models Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.
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