Deep Learning: Fundamentals and Applications
ΠΡΠΈΡΠΊΠΎ Π·Π° ΡΠ°Π·ΠΈ Π΅Π»Π΅ΠΊΡΡΠΎΠ½Π½Π° ΠΊΠ½ΠΈΠ³Π°
Deep learning belongs to a larger family of machine learning approaches that are founded on artificial neural networks and representation learning. This family of methods is known as deep learning. There are three different ways to learn: supervised, semi-supervised, and unsupervised.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Deep learning
Chapter 2: Machine learning
Chapter 3: Neural coding
Chapter 4: Scale space
Chapter 5: Compressed sensing
Chapter 6: Reservoir computing
Chapter 7: Echo state network
Chapter 8: Stochastic parrot
Chapter 9: Differentiable programming
Chapter 10: Liquid state machine
(II) Answering the public top questions about deep learning.
(III) Real world examples for the usage of deep learning in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of deep learning' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of deep learning.
