Introduction to Deep Learning

Introduction to Deep Learning[PDF / Epub] ☀ Introduction to Deep Learning By Eugene Charniak – Oaklandjobs.co.uk A project based guide to the basics of deep learningThis concise, project driven guide to deep learning takes readers through a series of program writing tasks that introduce them to the use of deep l A project based guide to the basics of deep learningThis concise, project driven guide to deep learning takes readers through a series of program writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural Introduction to eBook ì language processing, and reinforcement learning The author, a longtime artificial intelligence researcher specializing in natural language processing, covers feed forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence to sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open source machine learning framework I find I learn computer science material best by sitting down and writing programs, the author writes, and the book reflects this approachEach chapter includes a programming project, exercises, and references for further reading An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python The book can be used in both undergraduate and graduate courses practitioners will find it an essential reference. The description of the algorithms was good but I don t like code it was not very fluent for me. This review is written from the perspective of an intermediate machine learning student Specifically I have taken a year of rigorous university machine learning coursework and a semester of general AI coursework The last month of my most recent ML course focused on deep learning, but implementation was limited to writing a FF NN with Numpy We did not discuss or use TF, Keras, or any other deep learning libraries.This book picked up roughly where my class left off It follows a logical sequenc This review is written from the perspective of an intermediate machine learning student Specifically I have taken a year of rigorous university machine learning coursework and a semester of general AI coursework The last month of my most recent ML course focused on deep learning, but implementation was limited to writing a FF NN with Numpy We did not discuss or use TF, Keras, or any other deep learning libraries.This book picked up roughly where my class left off It follows a logical sequence of major domains in deep learning, and introduces TF from the first chapters at a level approachable to a complete novice like me The figures provided were largely helpful for synthesizing the ideas, and the author s own takes on the algorithms and how they function were often, but not always, useful.My foremost complaints have to be about the quality of the code provided and the editing of the book overall First, the code provided is no longer current recommended TF usage I know this is difficult from the author s perspective However, it took me hours of reading the TF documentation to learn just how much of the provided code, including sessions, variable scopes, etc are staged for obsolescence Second, the code itself is incomplete and inconsistent in places, occasionally with mismatched variables even The worst section of the book in these regards was chapter 4 covering RNNs The code provided is NOT sufficient to reconstruct his example, especially because he does not explicitly discuss the dimensions of the tensors involved perhaps the most crucial of details I will say the book improved in the remainder of the chapters in these regards and does includecomplete code and discussion in the final chapters Third, there are general typos and poorly edited sections throughout, including lines similar to I can t remember this easily searched detail off the top of my head, so we will skip it, followed a few lines later by, I actually did look it up and this was the answer In summary, it was a useful introduction to TF and many of these deep learning disciplines, but I ended up running to the official TF tutorials and Github for answersoften than I d like I wish the code were current and ready for TF 2.0 An Excellent Book to get an Introduction to Deep Learning I took this book to help me understand about Deep Learning within A.I This might be a small scaffold for my PhD thesis I think if you re new to this topic, this would be a good introduction I think, the references and citations are useful to understand researchers and scholars involved in this field Deus Vult,Gottfried

Introduction to Deep Learning eBook ´ Introduction to
    Introduction to Deep Learning eBook ´ Introduction to programming project, exercises, and references for further reading An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python The book can be used in both undergraduate and graduate courses practitioners will find it an essential reference. The description of the algorithms was good but I don t like code it was not very fluent for me. This review is written from the perspective of an intermediate machine learning student Specifically I have taken a year of rigorous university machine learning coursework and a semester of general AI coursework The last month of my most recent ML course focused on deep learning, but implementation was limited to writing a FF NN with Numpy We did not discuss or use TF, Keras, or any other deep learning libraries.This book picked up roughly where my class left off It follows a logical sequenc This review is written from the perspective of an intermediate machine learning student Specifically I have taken a year of rigorous university machine learning coursework and a semester of general AI coursework The last month of my most recent ML course focused on deep learning, but implementation was limited to writing a FF NN with Numpy We did not discuss or use TF, Keras, or any other deep learning libraries.This book picked up roughly where my class left off It follows a logical sequence of major domains in deep learning, and introduces TF from the first chapters at a level approachable to a complete novice like me The figures provided were largely helpful for synthesizing the ideas, and the author s own takes on the algorithms and how they function were often, but not always, useful.My foremost complaints have to be about the quality of the code provided and the editing of the book overall First, the code provided is no longer current recommended TF usage I know this is difficult from the author s perspective However, it took me hours of reading the TF documentation to learn just how much of the provided code, including sessions, variable scopes, etc are staged for obsolescence Second, the code itself is incomplete and inconsistent in places, occasionally with mismatched variables even The worst section of the book in these regards was chapter 4 covering RNNs The code provided is NOT sufficient to reconstruct his example, especially because he does not explicitly discuss the dimensions of the tensors involved perhaps the most crucial of details I will say the book improved in the remainder of the chapters in these regards and does includecomplete code and discussion in the final chapters Third, there are general typos and poorly edited sections throughout, including lines similar to I can t remember this easily searched detail off the top of my head, so we will skip it, followed a few lines later by, I actually did look it up and this was the answer In summary, it was a useful introduction to TF and many of these deep learning disciplines, but I ended up running to the official TF tutorials and Github for answersoften than I d like I wish the code were current and ready for TF 2.0 An Excellent Book to get an Introduction to Deep Learning I took this book to help me understand about Deep Learning within A.I This might be a small scaffold for my PhD thesis I think if you re new to this topic, this would be a good introduction I think, the references and citations are useful to understand researchers and scholars involved in this field Deus Vult,Gottfried "/>
  • Hardcover
  • 192 pages
  • Introduction to Deep Learning
  • Eugene Charniak
  • English
  • 07 April 2018
  • 0262039516