Ebook TensorFlow for Deep Learning : From Linear Regression to Reinforcement Learning, by Bharath Ramsundar
Pourquoi apprendre plus de publications vous fournir beaucoup plus de prospects pour réussir? Vous comprenez, le supplémentaire que vous examinez les livres, le supplément vous obtiendrez certainement des leçons extraordinaires et de l'expertise. Beaucoup de gens avec plusieurs publications à lire complète sera certainement agir différent à des personnes qui ne l'aiment pas beaucoup. Afin de vous offrir une bien meilleure chose à faire tous les jours, TensorFlow For Deep Learning : From Linear Regression To Reinforcement Learning, By Bharath Ramsundar peut être choisi comme ami d'investir le temps libre.
TensorFlow for Deep Learning : From Linear Regression to Reinforcement Learning, by Bharath Ramsundar
Ebook TensorFlow for Deep Learning : From Linear Regression to Reinforcement Learning, by Bharath Ramsundar
TensorFlow For Deep Learning : From Linear Regression To Reinforcement Learning, By Bharath Ramsundar . Un jour, vous découvrirez une nouvelle expérience et aussi une expertise en dépensant encore plus d' argent. Mais quand? Pensez - vous que vous avez besoin d'acquérir ces tous les besoins en ayant beaucoup d' argent? Pourquoi ne pas essayer d'obtenir quelque chose de base au début? C'est quelque chose qui vous mènera à comprendre encore plus sur le monde, l' expérience, certains domaines, l' histoire, le divertissement, ainsi que beaucoup plus? Il est votre temps pour poursuivre l' examen de l' habitude. L' un des guides que vous pouvez prendre plaisir à est maintenant TensorFlow For Deep Learning : From Linear Regression To Reinforcement Learning, By Bharath Ramsundar ci - dessous.
Vérifier une publication est en outre type de bien meilleur remède quand on n'a pas prêt adéquat ou de temps pour obtenir votre propre aventure. Ceci est l'un des facteurs que nous révélons le TensorFlow For Deep Learning : From Linear Regression To Reinforcement Learning, By Bharath Ramsundar en tant que votre ami à investir le temps. Pour encore plus collections depictive, ce livre offre non seulement sa source stratégique de livre. Il peut être un bon ami, grand ami avec beaucoup d'expertise.
Comme on le comprend, pour compléter ce livre, vous pourriez ne pas avoir à le faire en même temps dans une journée. Faire les activités le long de la journée peut vous faire sentir tellement ennuyé. Si vous essayez de forcer la lecture, vous pourriez favoriser d'autres tâches amusantes. Cependant, parmi les concepts que nous voulons que vous ayez cette publication est que ce ne sera certainement pas vous faire sentir ennuyé. se sentir vraiment fatigué lors de l'examen sera juste à moins que vous n'avez pas comme guide. TensorFlow For Deep Learning : From Linear Regression To Reinforcement Learning, By Bharath Ramsundar offre vraiment tout ce que tout le monde désire.
Les sélections de mots, dictions, ainsi que la façon dont l'auteur transmet le message ainsi que la leçon aux téléspectateurs sont vraiment faciles à comprendre. Donc, quand vous vous sentez négatif, on ne pouvait pas croire si dur sur ce livre. Vous pouvez prendre plaisir à prendre ainsi que plusieurs de la leçon fournit. L'utilisation de la langue quotidienne rend le TensorFlow For Deep Learning : From Linear Regression To Reinforcement Learning, By Bharath Ramsundar de premier plan dans l'expérience. Vous pouvez trouver les moyens de vous faire une déclaration appropriée de vérifier la conception. Eh bien, ce n'est pas très facile difficile si vous vraiment n'aimez pas l'analyse. Il sera certainement pire. Pourtant, ce livre vous aidera à se sentir vraiment différent de ce que vous pouvez vraiment sentir ainsi.
Détails sur le produit
Broché: 300 pages
Editeur : O'Reilly Media, Inc, USA (4 avril 2018)
Langue : Anglais
ISBN-10: 1491980451
ISBN-13: 978-1491980453
Dimensions du produit:
17,5 x 1,3 x 23,1 cm
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This book has one page for every Data Scientific topic, each of which could take a book of its own. It is too short even for a review, not speaking about a textbook. Absolutely useless.
I am happy to have my book. The content is clear and rich. However on the delivery of my new book, some of the pages were crinkled.
Good fundamentals to understand how to code and play with tensors and python for Deep Learning
Had high expectations but the book totally ruined them. The book does not covers concepts which you might already know. Finally I had no idea whether this book is intended to teach more of tensorflow concepts or deep learning paradigms. In my opinion it failed to do both. The book starts of well explaining the core concepts of Tensorflow. But as you go into individual chapters for sequential processing or vision, they just shared the code and did a very poor job in explaining the Tensorflow Api. It is equivalent to seeing some code on github and try learning yourself using google.Since I already understand the core concepts like sessions/graphs this book is of no use to me. The worst part is that the code samples are the most basic you could get. For text processing they took Tensorflow.org tutorial and diluted it so much there is hardly anything to learn on text processing side.Essentially this book = basic concepts (which most people already know) + aggregation of github codes for each subject ( which are too basic and you can easily find much much better repositories online).The worst part is even the code samples are buggy. Even the basic linear regression code is wrong and does not optimise unless you change that. In my opinion the text processing code is wrong too, but I'm not too sure of it.
TensorFlow for Deep Learning by Ramsundar and Zadeh is 230 pages of great machine learning content that should compliment any data science library. If I had to complain, my largest gripe would be the strong bias toward the mathematical details of tensor calculus. Not that math is undesirable, but with only 230 pages to spare I felt that equations were often thrown out without adequate explanation.The introduction also comes on a little strong. The first chapter is named “Machine Learning Eats Computer Scienceâ€. Perhaps a better title would be “Deep Learning Hype at Full Throttleâ€. But let’s be real, deep learning is a subset of computer science – very useful for certain tasks and useless for others. The text would have you believe that deep learning is some new alien technology that is not related to algorithmic approaches at all.But this book has it where it counts. The structure of the chapters is laid out in a very intuitive manner that demonstrates that these authors know exactly what they are talking about and are eager to share the knowledge. First, Tensorflow primitive are introduced, next linear regression is explored, then on to fully connected deep networks. The fun really begins next with hyperparameter optimization, convolutional neural networks, recurrent neural networks, reinforced learning, and finally training. Relevant topics, logistically ordered, and adequately explained.It’s not a perfect book, however. Some of the diagrams and graphs have descriptions that refer to colors, yet all the images printed in the book are black and white. This makes some figures very difficult to interpret.The ending chapter on ethics also shares a lot in common with the hyped-up introduction – for example, dramatic fretting over sentient war terminators and suggesting quitting your job over questionable learning applications is a little much. In truth, governments leveraging technology to suppress freedom should be our concern – and this has been true for all time and all technologies. Enforceable checks and balances of a structured government have always been the best defense, not quitting a job… but I digress.Overall a very worthy addition to a data science library. You’ll probably want to have at least an introductory grasp on the Tensorflow and deep learning before reading this book, but it’s a great next step. Highly recommended.
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