Kalbim Sende Kaldı - Judith McNaught ücretsiz kitap indir

Kathryn Travers _at_ravers — Overall, I thought that Tuesdays with Morrie was a wonderful, inspirational book about life. The book is outstanding as a beloved professor; Morrie gives his input and insight about the meanings of life. Whilst reading this book, small confusions and anxieties about life is somewhat answered as Morrie lectures Mitch about the significance of life.

David Ellis _avid_andall_llis — For those who get bored quickly in one genre. This was fun. The heart of the book, to me, was at the heart. The apex of the book hit the strong stuff and let things diffuse from there. The narrator in Tim Cavendish's story was a hoot, but all were enjoyable in different ways.

李 育靜 yuchinglee8198 — The bible of learning theory, the book every machine learning theorist should have in his/her private library. Well ... let's be more precise! What is this book about? This book is about the theory of machine learning, and more specifically, the theory of regression when you do not have any strong assumption on the model of the data (so, no Gaussian assumption, no Linear assumption, etc ...). Most of the book is about proving that this specific approach is consistent and then book provides a convergence rate for that method. You can see these kinds of arguments for local averagers (K-NN, Kernel method, Partitioning), Least Squares methods (with and without regularization), and several other topics. If you are interested in theory of machine learning, you should read at least some parts of this book - even though the book is not an easy read (I plan to understand the whole book in the next few years!!!). Remember that this book is not an algorithmic book. So, you should not read it to learn or even understand "new" machine learning methods. Use Hastie, Tibshirani, and Friedman's book instead which is an excellent book on its own (though not the easiest one anyway). Also note that this book does not consider classification problems so much. However, the theory -as I partially understand it- should not be that different. If you are really interested about the theory of classification, you may like to read Deroye, Gyorfi, and Lugosi, A Probabilistic Theory of Pattern Recognition, 1997 instead.