Download Free In Pursuit Of Deep Learning Book in PDF and EPUB Free Download. You can read online In Pursuit Of Deep Learning and write the review.

"The purpose of this research is to understand how teachers in a college preparatory school who use adaptive expertise and varied assessments are creating a classroom environment that focuses on and develops students who are deep learners that can effectively solve 21st century problems. This qualitative case study utilized deep learning and adaptive expertise as the theoretical frameworks to ensure that the study was aligned and maintained an ethical approach that minimized bias. The fundamental research question that drives this study is: How do teachers in a college preparatory school foster deep learning in the classroom so that students can become 21st century learners, thinkers, and problem-solvers? After document reviews, observations, and interviews with six participants, seven themes and eight sub themes emerged through the analysis of the data. These themes included: 1) Administrative Support is Essential, 2) Teachers Must Be the Chief Learner, 3) Teachers Must Change The Nature of Planning and Work Assigned, 4) Critical Learning Environments Required, 5) Feedback/Assessment is Essential and Grading Must Be Fair, 6) Adaptive Expertise Habits Are Observed Daily, and 7) Obstacles to Deep Learning Exists. From these themes, five findings evolved: a) Teachers are the Single-Most Important Key to Promoting Deep Learning, b) Feedback is Essential and Grading Must Be Fair, c) Motivation Must Be More Intrinsic Than Extrinsic, d) Professional Development Must Be High Quality, Ongoing, and Utilize Adaptive Expertise Habits and Deep Learning Processes, and e) There Needs to Be a Balance Between the Art and Science of Teaching. Ultimately, teachers are the most essential key to transforming the classroom into places of deep learning at this independent, college preparatory school"--Author's abstract.
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
"The best book on high school dynamics I have ever read."--Jay Mathews, Washington Post An award-winning professor and an accomplished educator take us beyond the hype of reform and inside some of America's most innovative classrooms to show what is working--and what isn't--in our schools. What would it take to transform industrial-era schools into modern organizations capable of supporting deep learning for all? Jal Mehta and Sarah Fine's quest to answer this question took them inside some of America's most innovative schools and classrooms--places where educators are rethinking both what and how students should learn. The story they tell is alternately discouraging and hopeful. Drawing on hundreds of hours of observations and interviews at thirty different schools, Mehta and Fine reveal that deeper learning is more often the exception than the rule. And yet they find pockets of powerful learning at almost every school, often in electives and extracurriculars as well as in a few mold-breaking academic courses. These spaces achieve depth, the authors argue, because they emphasize purpose and choice, cultivate community, and draw on powerful traditions of apprenticeship. These outliers suggest that it is difficult but possible for schools and classrooms to achieve the integrations that support deep learning: rigor with joy, precision with play, mastery with identity and creativity. This boldly humanistic book offers a rich account of what education can be. The first panoramic study of American public high schools since the 1980s, In Search of Deeper Learning lays out a new vision for American education--one that will set the agenda for schools of the future.
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
"In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, etc. This book presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction"--
"Foundations of Deep Learning" offers an erudite exploration into the dynamic landscape of artificial intelligence (AI) and deep learning, authored by Tapomoy Adhikari, an autonomous researcher in the field of Computer Science and Engineering. This scholarly work provides a comprehensive resource suitable for individuals at various stages of expertise, ranging from neophytes to seasoned practitioners within the domain of neural networks. Commencing with an introductory exposition, the book elucidates fundamental principles integral to deep learning. Subsequently, it undertakes a rigorous examination of neural network architectures, elucidating their constituent elements, activation functions, and optimization methodologies. The discourse extends to encompass the intricate mechanisms of backpropagation, a cornerstone process in neural network training. Further chapters delve deeply into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), elucidating their pivotal roles across diverse applications such as computer vision and natural language processing. Noteworthy concepts explored include Generative Adversarial Networks (GANs), Attention Mechanisms, and Transfer Learning, furnishing readers with a comprehensive toolkit to address real-world challenges. In light of burgeoning ethical concerns within the AI landscape, the book offers nuanced insights into ethical considerations pertinent to deep learning. Emphasis is placed on responsible AI model development and its societal implications. The discourse extends to encompass the domain of Natural Language Processing (NLP) integrated with deep learning, elucidating concepts such as word embeddings and sequence-to-sequence models, alongside the transformative potential of attention mechanisms. Deep Reinforcement Learning, a pivotal paradigm underpinning gaming AI and autonomous systems, undergoes meticulous scrutiny, equipping readers with the requisite knowledge to navigate this burgeoning field. As the narrative culminates, readers are prompted to contemplate the future trajectory of deep learning, exploring themes such as neuro-symbolic integration, the potential impact of quantum computing, and the ethical imperatives guiding AI development. "Foundations of Deep Learning" transcends mere instructional discourse, serving as a scholarly compendium elucidating the inner workings of AI architectures shaping contemporary society. Augmented with code snippets, diagrams, and illustrative case studies, this academic endeavor facilitates a practical and accessible understanding of complex concepts. Irrespective of readers' academic or professional affiliations, be it as students, researchers, or engineers, this scholarly treatise equips them with the requisite knowledge and methodologies to navigate the ever-evolving landscape of neural networks.
Just like any new technology, what perplexes many is the question of how to apply Deep Learning in a business context. Technology that is disruptive does not automatically imply that the development of valuable use cases are apparent. For years, many people could not figure out how to monetize the World Wide Web. We are in that same situation with Deep Learning AI. The developments are mind-boggling but the monetization is far from being obvious.Deep Learning Artificial Intelligence involves the interplay of Computer Science, Physics, Biology, Linguistics and Psychology. In addition to that, it is technology that can be extremely disruptive. Furthermore, the ramifications to society and even our own humanity can be immense. There are few subjects that are as captivating and as consequential as this. Surprisingly, there is very little that is written about this new technology in a more comprehensive and cohesive way. This book is an opinionated take on the developments of Deep Learning AI.
The author of the best-selling What the Best College Teachers Do is back with more humane, doable, and inspiring help, this time for students who want to get the most out of college—and every other educational enterprise, too. The first thing they should do? Think beyond the transcript. The creative, successful people profiled in this book—college graduates who went on to change the world we live in—aimed higher than straight A’s. They used their four years to cultivate habits of thought that would enable them to grow and adapt throughout their lives. Combining academic research on learning and motivation with insights drawn from interviews with people who have won Nobel Prizes, Emmys, fame, or the admiration of people in their field, Ken Bain identifies the key attitudes that distinguished the best college students from their peers. These individuals started out with the belief that intelligence and ability are expandable, not fixed. This led them to make connections across disciplines, to develop a “meta-cognitive” understanding of their own ways of thinking, and to find ways to negotiate ill-structured problems rather than simply looking for right answers. Intrinsically motivated by their own sense of purpose, they were not demoralized by failure nor overly impressed with conventional notions of success. These movers and shakers didn’t achieve success by making success their goal. For them, it was a byproduct of following their intellectual curiosity, solving useful problems, and taking risks in order to learn and grow.