`Download Pdf ☊ Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms ⇱ PDF eBook or Kindle ePUB free

I am finished with the number of chapters that have been released so far There have been three in total The material is a little rough but it is an early release One should have some basic understanding of statistics and probability before attempting to digest the material Looking forward to the additional chapters. This book strikes a good balance between the DL textbooks which are quite dense and the many practitioners guides which have code examples but are light on theory math There are equations here as well as code I ve been checking this one out from the library, but I m going to go ahead an order my own copy This book strikes a good balance between the DL textbooks which are quite dense and the many practitioners guides which have code examples but are light on theory math There are equations here as well as code I ve been checking this one out from the library, but I m going to go ahead an order my own copy If you expect code example, you would be disappointed This book is very good at covering fundamentals, which I like I suggest this book as a supplement with other deep learning book. Strengths Gives a really good overview of computer vision history and why traditional machine learning methods don t perform as good as convolutional networks The section that talks about Gradient Descent is really well explained and destroy some myths around gradient descent even though there is no math Gives a clear and intuitive idea of how convolutional layers can capture patterns in images It includes attention methods for NLP Weaknesses Lacks math and precise definitions but that Strengths Gives a really good overview of computer vision history and why traditional machine learning methods don t perform as good as convolutional networks The section that talks about Gradient Descent is really well explained and destroy some myths around gradient descent even though there is no math Gives a clear and intuitive idea of how convolutional layers can capture patterns in images It includes attention methods for NLP Weaknesses Lacks math and precise definitions but that is ok if the book was done for beginners It uses tensorflow for all examples which turns hard and cumbersome for beginners It doesn t talk about other frameworks some of the examples could have been written on top of tensorflow but using keras tensorlearn or using pytorch Code Snippets are long, hard to follow and sometimes present errors Some images have font size really small which turns impossible to read not read chapter 8 good start point to read open AI gym This book does not provide much details about each algorithm It basically just mentions what it is Therefore, read multiple books at the same time is a great help to understand how deep learning works Some codes syntax are old and should be corrected However, it definitely worths time reading the example codes. As for me, it s a slightly complicated The math basic is explained in a quite poor and boring manner The another disadvantage is a lack of real world examples It s a challenge to connect a pure formulas with high level ML algorithms I agree the book might be useful however I don t like so academic style As result this is only two stars I can t give. This review has been hidden because it contains spoilers To view it, click here first book Chapters are of varying quality, in particular the last one on deep reinforcement learning written by a contributing author doesn t jibe well with the rest of the book. Its one of the few books, that combines practical and theoretical information in a very balanced way The first half of the book for me was very easy to follow But I need to add, before the book, I have finished Andrew Ng s 16 week Machine Learning course, read a couple other books on Data Science and did some basic math coding on the various ML AI areas Somehow, up to Convolutional Neural Networks %50 of the book , there is a very good overview of what Gradient Descent is and how to impleme Its one of the few books, that combines practical and theoretical information in a very balanced way The first half of the book for me was very easy to follow But I need to add, before the book, I have finished Andrew Ng s 16 week Machine Learning course, read a couple other books on Data Science and did some basic mathcoding on the various ML AI areas Somehow, up to Convolutional Neural Networks %50 of the book , there is a very good overview of what Gradient Descent is and how to implement and use it After CNN things getserious and it moves onto relatively newly discovered and production level state of the art models like the basic model powering Google Translate The last chapter is about Deep Reinforcement Learning Deep Minds astonishing model for all Atari games and ends with very recent topics like Async Advantage Actor Critic Agents and UNREAL I would be happier if I would seecomputer vision related models and problems instead of sentiment and sequence analysis but its completely a personal preference I strongly recommend this book if you have interest in Deep Learning `Download Pdf ☆ Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms ⇹ FUNDAMENTALS OF DEEP LEARNING Paperback NotRetrouvez FUNDAMENTALS OF DEEP LEARNING Paperback Jan ,BUDUMA et des millions de livres en stock surAchetez neuf ou d occasion Fundamentals of Deep Learning Designing Next GenerationDeep Learning Fundamentals of Deep Learning for Beginners Artificial Intelligence BookEnglish Edition Rudolph Russell Format Kindle , Suivant Commentaires client , surtoiles , sur Evaluations clientstoiles % % %toiles % % %toiles % % %toiles % % %toile % % % Comment est ce quprocde Fundamentals of Deep Learning Buduma, NikhilNotRetrouvez Fundamentals of Deep Learning et des millions de livres en stock surAchetez neuf ou d occasion Fundamentals of Deep Learning and Computer Vision AAchetez et tlchargez ebook Fundamentals of Deep Learning and Computer Vision A Complete Guide to become an Expert in Deep Learning and Computer Vision English Edition Boutique Kindle Artificial IntelligenceServeur de Pages Professionnelles Individuelles Serveur de Pages Professionnelles Individuelles Fundamentals of Deep Learning Analytics Vidhya Key takeaways from Fundamentals of Deep Learning Course These deep learning algorithms are powered by techniques like Convolutional Neural Networks CNN , Recurrent Neural Networks RNN , Long Short Term Memory LSTM , etc