☃ [PDF / Epub] ☂ Fundamentals of Deep Learning By Nikhil Buduma ✑ – Dailytradenews.co.uk

  • Kindle Edition
  • 298 pages
  • Fundamentals of Deep Learning
  • Nikhil Buduma
  • English
  • 02 December 2018
  • 9781491925614

10 thoughts on “Fundamentals of Deep Learning

  1. says:

    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 get serious 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 see computer 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.

  2. says:

    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.

  3. says:

    When in school, we often used a term to label things that were hard to comprehend OHT or Over Head Transmission Essentially, concepts that the brain failed to catch This book felt the same at many levels It was great once again encounter calculus, vectors, transforms and matrices, long after school and college days I can t say I understood them with the same rigor as when in school though Reading this book didn t help me understand Neural Networks all that much as it made me familiar with the associated terminology gradient descent, soft max output layer, feed forward, Sigmoid Tanh ReLU, Training Validation Test data sets, overfitting, L1 L2 regularization Max norm constraints Dropout, tensor Flow, Stochastic Gradient Descent, local minima, learning rate adaptation, Convolution networks, Principal Component Analysis, Word2Vec, LSTM, SkipGram, seq2seq, Beam search, vanishing gradients, RNN, NTM, Differential Neural Computers, Markov Decision Process, Explore vs Exploit, Deep Q Network, Deep Recurrent Q Networks DRQN , Asynchronous Advantage Actor Critic Agent A3C , UNsupervised REinforcement and Auxiliary Learning UNREAL one gets the idea.I found the book was rich on concepts and ideas but not as lucid on explanations I had to refer to the web several times to understand what author was trying to say and found some of the explanations on the web easier to comprehend On the social side, this book makes it quite obvious why the divide between the haves and the have nots in our society continues to deepen and widen irreparably and at great pace Machine Learning, which is increasingly becoming the bedrock of technical solutions and business strategy, is a very complex topic Unlike the complexities of the Industrial Age, much of which could be overcome with on the job training, the new technical concepts require rigorous technical education in advanced computational, statistical, and mathematical topics This education is not an easily available or economically viable option for majority of the worlds population In addition, even if we take into account that not everyone has to learn these subjects one still has to grapple with the envy generated when regular folks have to live in the shadow of the ensuing monetary success of the masters of these sciences However, not all is lost This technology may not be within reach of all but its outcomes have the ability to influence all lives just as much These outcomes are of human choosing Whether these will deepen the divide by trying to sell to those who can buy or bridge the gaps in human condition by creating solutions to knowledge, goods and service distribution, is a choice we as a society need to make Our world, is our responsibility.

  4. says:

    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

  5. says:

    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.

  6. says:

    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 .

  7. says:

    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.

  8. says:

    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.

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