深度学习实战：基于案例理解DNN

需积分: 9 201 浏览量更新于2024-07-18 1 收藏 12.58MB PDF 举报

"Applied Deep Learning: Understanding DNN" 是一本深入探讨深度学习的书籍，由Umberto Michelucci撰写。该书以案例为基础，引导读者理解深度神经网络（DNN）的工作原理和应用。书中涵盖了TensorFlow框架、神经网络的基础、网络训练策略、正则化技术、超参数调优、卷积神经网络（CNN）以及各种实际应用场景。在深度学习领域，本书首先介绍了深度神经网络的基本概念，包括多层感知器（MLP）和反向传播算法，这些都是构建和训练DNN的基础。接着，通过TensorFlow这一强大的开源库，读者将学习如何构建和实现这些模型，从而掌握实际操作中的技巧。正则化和超参数调节是防止过拟合、提高模型泛化能力的关键。书中详细讲解了L1和L2正则化、Dropout等方法，并指导读者如何根据具体问题选择合适的超参数。此外，书中还涵盖了数据预处理、优化算法（如梯度下降及其变种）和损失函数的选择，这些都是训练高效DNN的重要组成部分。卷积神经网络（CNN）部分，作者深入剖析了CNN在图像识别和计算机视觉任务中的应用。读者将了解卷积层、池化层、全连接层的作用，以及如何利用这些层构建能够识别图像特征的网络。此外，书中可能还会讨论其他领域的应用，如自然语言处理（NLP）中的循环神经网络（RNN）和长短时记忆网络（LSTM）。通过案例研究，本书将理论与实践相结合，让读者不仅理解深度学习的理论基础，还能学会将其应用于实际问题中。这可能包括但不限于图像分类、文本分析、推荐系统等。最后，书中可能还会讨论一些先进的主题，如生成对抗网络（GAN）、强化学习（RL）以及深度学习模型的部署和监控。 "Applied Deep Learning: Understanding DNN" 是一本全面且实用的深度学习指南，适合对AI和机器学习感兴趣的初学者及有一定经验的从业者，旨在通过案例教学帮助读者深入理解和应用深度神经网络。

xviii

cannot comprehend why a large learning rate will make your model (strictly speaking,

the cost function) diverge, if you don’t know how the gradient descent algorithm works

mathematically. In all real-life projects, you will not have to calculate partial derivatives

or complex sums, but you will have to understand them to be able to evaluate what

can work and what cannot (and especially why). Appreciating why a library such as

TensorFlow makes your life easier is only possible if you try to develop a trivial model

with one neuron from scratch. It is a very instructive thing to do, and I will show you how

in Chapter 10. Once you have done it once, you will remember it forever, and you will

really appreciate libraries such as TensorFlow.

I suggest that you really try to understand the mathematical underpinnings

(although this is not strictly necessary to profit from the book), because they will allow

you to fully understand many concepts that otherwise cannot be understood completely.

Machine learning is a very complicated subject, and it is utopic to think that it is possible

to understand it thoroughly without a good grasp of mathematics or Python. In each

chapter, I highlight important tips to develop things efficiently in Python. There is no

statement in this book that is not backed up by concrete examples and reproducible

code. I will not discuss anything without offering related real-life examples. In this way,

everything will make sense immediately, and you will remember it.

Take the time to study the code that you find in this book and try it for yourself. As

every good teacher knows, learning works best when students try to resolve problems

themselves. Try, make mistakes, and learn. Read a chapter, type in the code, and try to

modify it. For example, in Chapter 2, I will show you how to perform binary classification

recognition between two handwritten digits: 1 and 2. Take the code and try two different

digits. Play with the code and have fun.

By design, the code that you will find in this book is written as simply as possible. It

is not optimized, and I know that it is possible to write much better-performing code,

but by doing so, I would have sacrificed clarity and readability. The goal of this book is

not to teach you to write highly optimized Python code; it is to let you understand the

fundamental concepts of the algorithms and their limitations and give you a solid basis

with which to continue your learning in this field. Regardless, I will, of course, point out

important Python implementation details, such as, for example, how you should avoid

standard Python loops as much as possible.

All the code in this book is written to support the learning goals I have set for each

chapter. Libraries such as NumPy and TensorFlow have been recommended because

they allow mathematical formulations to be translated directly into Python. I am aware

inTRoduCTion

xix

of other software libraries, such as TensorFlow Lite, Keras, and many more that may

make your life easier, but those are merely tools. The significant difference lies in your

ability to understand the concepts behind the methods. If you get them right, you can

choose whatever tool you want, and you will be able to achieve a good implementation.

If you don’t understand how the algorithms work, no matter the tool, you will not be able

to undertake a proper implementation or a proper error analysis. I am a fierce opponent

of the concept of data science for everyone. Data science and machine learning are

difficult and complex subjects that require a deep understanding of the mathematics

and subtelties behind them.

I hope that you will have fun reading this book (I surely had a lot in writing it) and

that you will find the examples and the code useful. I hope, too, that you will have many

Eureka! moments, wherein you will finally understand why something works the way

you expect it to (or why it does not). I hope you will find the complete examples both

interesting and useful. If I help you to understand only one concept that was unclear to

you before, I will be happy.

There are a few chapters of this book that are more mathematically advanced. In

Chapter 2, for example, I calculate partial derivatives. But don’t worry, if you don’t

understand them, you can simply skip the equations. I have made sure that the main

concepts are understandable without most of the mathematical details. However, you

should really know what a matrix is, how to multiply matrices, what a transpose of a

matrix is, and so on. Basically, you need a good grasp of linear algebra. If you don’t

have one, I suggest you review a basic linear algebra book before reading this one. If

you have a solid linear algebra and calculus background, I strongly advise you not to

skip the mathematical parts. They can really help in understanding why we do things in

specific ways. For example, it will help you immensely in understanding the quirks of

the learning rate, or how the gradient descent algorithm works. You should also not be

scared by a more complex mathematical notation and feel confident with an equation

as complex as the following (this is the mean square error we will use for the linear

regression algorithm and will be explained in detail later, so don’t worry if you don’t

know what the symbols mean at this point):

Jw w

yfwwx

()

You should understand and feel confident with such concepts as a sum or a

mathematical series. If you feel unsure about these, review them before starting the

book; otherwise, you will miss some important concepts that you must have a firm

inTRoduCTion

grasp on to proceed in your deep-learning career. The goal of this book is not to give

you a mathematical foundation. I assume you have one. Deep learning and neural

networks (in general, machine learning) are complex, and whoever tries to convince you

otherwise is lying or doesn’t understand them.

I will not spend time in justifying or deriving algorithms or equations. You will have

to trust me there. Additionally, I will not discuss the applicability of specific equations.

For those of you with a good understanding of calculus, for example, I will not discuss

the problem of the differentiability of functions for which we calculate derivatives.

Simply assume that you can apply the formulas I give you. Many years of practical

implementations have shown the deep-learning community that those methods and

equations work as expected and can be used in practice. The kind of advanced topics

mentioned would require a separate book.

In Chapter 1, you will learn how to set up your Python environment and what

computational graphs are. I will discuss some basic examples of mathematical

calculations performed using TensorFlow. In Chapter 2, we will look at what you can

do with a single neuron. I will cover what an activation function is and what the most

used types, such as sigmoid, ReLU, or tanh, are. I will show you how gradient descent

works and how to implement logistic and linear regression with a single neuron and

TensorFlow. In Chapter 3, we will look at fully connected networks. I will discuss matrix

dimensions, what overfitting is, and introduce you to the Zalando dataset. We will

then build our first real network with TensorFlow and start looking at more complex

variations of gradient descent algorithms, such as mini-batch gradient descent. We will

also look at different ways of weight initialization and how to compare different network

architectures. In Chapter 4, we will look at dynamic learning rate decay algorithms,

such as staircase, step, or exponential decay, then I will discuss advanced optimizers,

such as Momentum, RMSProp, and Adam. I will also give you some hints on how to

develop custom optimizers with TensorFlow. In Chapter 5, I will discuss regularization,

including such well-known methods as l

, dropout, and early stopping. We will look at

the mathematics behind these methods and how to implement them in TensorFlow. In

Chapter 6, we will look at such concepts as human-level performance and Bayes error.

Next, I will introduce a metric analysis workflow that will allow you to identify problems

having to do with your dataset. Additionally, we will look at k-fold cross-validation as a

tool to validate your results. In Chapter 7, we will look at the black box class of problems

and what hyperparameter tuning is. We will look at such algorithms as grid and random

search and at which is more efficient and why. Then we will look at some tricks, such

inTRoduCTion

剩余424页未读，继续阅读

hxzyj

粉丝: 0
资源: 3

深度学习实战：基于案例理解DNN

Applied Deep Learning--2018

TensorFlow+1.x+Deep+Learning+Cookbook-Packt+Publishing(2017).epub

【Advanced】Image Depth Estimation in MATLAB: Using Deep Learning for Image Depth Estimation

Exploring the Future of YOLOv8: Cutting-edge Considerations in Deep Learning Object Detection ...

【Fundamentals】Voice Signal Synthesis in MATLAB: Understanding Speech Synthesis Technologies and ...

数学建模学习资料 神经网络算法 参考资料-Matlab 共26页.pptx

happybirthday2 升级版生日祝福密码0000(7).zip

ssm框架Java项目源码-基于web技术的税务门户网站的实现+vue毕设-大作业.zip

php7.4.33镜像7z压缩包

ssm框架Java项目源码-基于java的珠宝购物网站系统的建设+jsp毕设-大作业.zip

最新资源

数学建模学习资料神经网络算法参考资料-Matlab 共26页.pptx