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£30 today · 30-day average £29 · all-time low £27

No Starch Press Math For Deep Learning - Neural Network Guide

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Description

Master the mathematical foundations required to understand neural networks with Math For Deep Learning from No Starch Press. This book provides a practical approach to the essential mathematics used in deep learning through hands-on Python examples. You will cover vital topics including probability, statistics, linear algebra, differential calculus, and matrix calculus. Beyond theory, you will learn how to implement data flow in a neural network and master backpropagation and gradient descent. By using Python to work through the mathematics underlying these algorithms, you can build a fully-functional neural network yourself. The text also provides coverage of gradient descent and its common variations, making it a comprehensive resource for anyone looking to grasp the math behind AI and machine learning algorithms.

Key Features

Learn essential mathematical concepts including probability, statistics, and linear algebra through practical Python examples.

Master differential calculus and matrix calculus to understand the math used as a background for deep learning.

Gain hands-on experience implementing data flow in a neural network and understanding backpropagation.

Study gradient descent and its common variations to improve your understanding of how neural networks optimize.

Build a fully-functional neural network by using Python to work through the mathematics that underlies key algorithms.

Product Specifications

Colour
Multicolor
Format
paperback
Domain
Amazon UK
Release Date
02 December 2021
Listed Since
03 June 2021

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