Panda Ohana Publishing Machine Learning: An Applied Mathematics Introduction

If you want to go a bit mathematical and understand the math behind the well-known machine learning algorithm, go ahead and get this book, even though in certain areas it is a bit complex, I would have liked more elaboration on some concepts, but overall, this is really helpful.

Todo el tema de Deep Learning lo zanja el autor en un párrafo, en la página 170. Más o menos en plan (y no cito literalmente por brevedad): "habréis escuchado frecuentemente la frase Deep Learning, nadie se pone de acuerdo en qué significa, y en lo que se ponen de acuerdo es que hay redes neurales y muchas capas ocultas". Entiendo que el autor va de tío enrollado, pero aquí se ha pasado tres pueblos.

The book I received is quite new and looks authentic. Though it was a bit damage from corner but I am satisfied with the product.seller looks honest as it sell original products.

For me, this book fills several gaps in my understanding of machine learning (ML) topics. It is my introduction to ML concepts, methods, definitions, jargon etc. What's more important, and why I give this book 5 stars, is that there is no programming code in it. This means that the book focuses not on learning programming tools (like many articles online) but on the actual subjects and the math which ML is based on. The examples come from a wide variety of areas and problems, which encourages the reader to think broader on how to apply ML techniques. Dr. Wilmott has a lot of humor which makes the book an enjoyable and interesting read. I consider this book a solid foundation, and most importantly an inspiration for me to start exploring ML properly. I most definitely recommend this book!

Too complicated for an ‘introduction’ book. It could have had more explanations of ideas to make them easier to understand.

Very nice introduction to machine learning, applied mathematicians will appreciate it. And one of the few books on machine learning (maybe the only one) to suggest the use of Matthews Correlation as a measure of performance of a classifier.