Titlebook: Deep Learning Pipeline; Building a Deep Lear Hisham El-Amir,Mahmoud Hamdy Book 2020 Hisham El-Amir and Mahmoud Hamdy 2020 deep learning.dee

抗生素

Experiencing Aging: Analogue Versus VirtualIn Chapter 1, we saw an introduction to the deep learning field, to the theories that are the basis of the field. We also discussed the evolution of deep learning and the needed mathematical notation to succeed while reading the book, and we showed you how to install the environment needed for the projects in this book.

痛打

沒(méi)收

Proponent

樂(lè)章

Introductory Elements: Dependability IssuesFeature selection and engineering are important steps in a . and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.

BRIEF

Jean-Claude Geffroy,Gilles MotetWe’re still on the subject of gradient descent. But let’s now talk about gradient optimization, because of its importance to gradient descent. It is an optimization method for finding the minimum of a function, and it’s important in deep learning. It works to update the weights of the neural network through backpropagation.

implore

Marijn van Dongen,Wouter SerdijnIn the previous chapter, we showed you some advanced concepts in computer vision such as state-of-art architectures and the transfer learning approach. It is important for you to understand these concepts when you are about to build a model to do a certain task for you.

myriad

Setting Up Your EnvironmentNow that the history and math lessons are out of the way, we must be fair and give you a dessert, so let’s now prepare for the upcoming dirty work by setting our environment.

泥沼

獎(jiǎng)牌

Defining DataNow that you have a basic introduction to the world of . in general, and the world of . in particular, you should understand how this field is very important.