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To rise out of nothingness for the world to see To speak from my heart, a mind flowing free To release from my soul, my poetry To be liberated from within is How deep I can be To reveal a misunderstood, hidden part of me To dispel preconceived notions with reality To share my past, present, and sweet destiny To paint a portrait of my life is How deep I can be To write what I hear and then call it poetry is Only one example of How deep I can be In this deeply personal, yet profoundly universal collection, poet John D. Evans captures the life story of a young adult in urban America who was born in obscurity, overcame insecurity, and has lessons and stories to share. With titles like "I Remember When," "The Murder of Madam Roach," and "Reality Check," these thoughtful poems illuminate the "P's" and "D's" of life-Portraits, Power, Party, Dreams, Decisions, and Drama. Brave and unashamed, this ponder-worthy poetic journey will enlighten readers of all ages.
Award winning author Julia Spencer-Fleming does it again in this third mystery featuring Rev. Clare Fergusson and Sheriff Russ Van Alstyne in the small town of Millers Kill, N.Y. As the small town's gossip increasingly speculates about the Rev.'s ambigous relationship with the married Sheriff, a more urgent problem is the disappearance of the doctor of Millers Kill's free clinic, a town institution with roots in events from the 20s and 30s. Digging into the roots of these disturbing happenings, Russ and Clare find that painful events from the town's past can still roil the peace of Millers Kill. Out of the Deep I Cry is a 2005 Edgar Award Nominee for Best Novel.
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.
Legal Pathways to Deep Decarbonization in the United States provides a "legal playbook" for deep decarbonization in the United States, identifying well over 1,000 legal options for enabling the United States to address one of the greatest problems facing this country and the rest of humanity. The book is based on two reports by the Deep Decarbonization Pathways Project (DDPP) that explain technical and policy pathways for reducing U.S. greenhouse gas emissions by at least 80% from 1990 levels by 2050. This 80x50 target and similarly aggressive carbon abatement goals are often referred to as deep decarbonization, distinguished because it requires systemic changes to the energy economy. Legal Pathways explains the DDPP reports and then addresses in detail 35 different topics in as many chapters. These 35 chapters cover energy efficiency, conservation, and fuel switching; electricity decarbonization; fuel decarbonization; carbon capture and negative emissions; non-carbon dioxide climate pollutants; and a variety of cross-cutting issues. The legal options involve federal, state, and local law, as well as private governance. Authors were asked to include all options, even if they do not now seem politically realistic or likely, giving Legal Pathways not just immediate value, but also value over time. While both the scale and complexity of deep decarbonization are enormous, this book has a simple message: deep decarbonization is achievable in the United States using laws that exist or could be enacted. These legal tools can be used with significant economic, social, environmental, and national security benefits. Book Reviews "A growing chorus of Americans understand that climate change is the biggest public health, economic, and national security challenge our families have ever faced and they rightly ask, ''What can anyone do?'' Well, this book makes that answer very clear: we can do a lot as individuals, businesses, communities, cities, states, and the federal government to fight climate change. The legal pathways are many and the barriers are not insurmountable. In short, the time is now to dig deep and decarbonize." --Gina McCarthy, Former U.S. Environmental Protection Agency Administrator "Legal Pathways to Deep Decarbonization in the United States sets forth over 1,000 solutions for federal, state, local, and private actors to tackle climate change. This book also makes the math for Congress clear: with hundreds of policy options and 12 years to stop the worst impacts of climate change, now is the time to find a path forward." --Sheldon Whitehouse, U.S. Senator, Rhode Island "This superb work comes at a critical time in the history of our planet. As we increasingly face the threat and reality of climate change and its inevitable impact on our most vulnerable populations, this book provides the best and most current thinking on viable options for the future to address and ameliorate a vexing, worldwide challenge of extraordinary magnitude. Michael Gerrard and John Dernbach are two of the most distinguished academicians in the country on these issues, and they have assembled leading scholars and practitioners to provide a possible path forward. With 35 chapters and over 1,000 legal options, the book is like a menu of offerings for public consumption, showing that real actions can be taken, now and in the future, to achieve deep decarbonization. I recommend the book highly." --John C. Cruden, Past Assistant Attorney General, Environment and Natural Resources Division, U.S. Department of Justice "This book proves that we already know what to do about climate change, if only we had the will to do it. The path to decarbonization depends as much on removing legal impediments and changing outdated incentive systems as it does on imposing new regulations. There are ideas here for every sector of the economy, for every level of government, and for business and nongovernmental organizations, too, all of which should be on the table for any serious country facing the most serious of challenges. By giving us a sense of the possible, Gerrard and Dernbach and their fine authors seem to be saying two things: (1) do something; and (2) it''s possible. What a timely message, and what a great collection." --Jody Freeman, Archibald Cox Professor of Law and Founding Director of the Harvard Law School Environmental and Energy Law Program
Harness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of code Key Features: Discover how to apply state-of-the-art deep learning techniques to real-world problems Build and train neural networks using the power and flexibility of the fastai framework Use deep learning to tackle problems such as image classification and text classification Book Description: fastai is an easy-to-use deep learning framework built on top of PyTorch that lets you rapidly create complete deep learning solutions with as few as 10 lines of code. Both predominant low-level deep learning frameworks, TensorFlow and PyTorch, require a lot of code, even for straightforward applications. In contrast, fastai handles the messy details for you and lets you focus on applying deep learning to actually solve problems. The book begins by summarizing the value of fastai and showing you how to create a simple 'hello world' deep learning application with fastai. You'll then learn how to use fastai for all four application areas that the framework explicitly supports: tabular data, text data (NLP), recommender systems, and vision data. As you advance, you'll work through a series of practical examples that illustrate how to create real-world applications of each type. Next, you'll learn how to deploy fastai models, including creating a simple web application that predicts what object is depicted in an image. The book wraps up with an overview of the advanced features of fastai. By the end of this fastai book, you'll be able to create your own deep learning applications using fastai. You'll also have learned how to use fastai to prepare raw datasets, explore datasets, train deep learning models, and deploy trained models. What You Will Learn: Prepare real-world raw datasets to train fastai deep learning models Train fastai deep learning models using text and tabular data Create recommender systems with fastai Find out how to assess whether fastai is a good fit for a given problem Deploy fastai deep learning models in web applications Train fastai deep learning models for image classification Who this book is for: This book is for data scientists, machine learning developers, and deep learning enthusiasts looking to explore the fastai framework using a recipe-based approach. Working knowledge of the Python programming language and machine learning basics is strongly recommended to get the most out of this deep learning book.