Java深度学习实战：用流行的AI库解决实际问题

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"《深度学习 - Java实践神经网络》是一本深入浅出的指南，专为解决预测建模问题而设计，利用当前流行的Java机器学习库。作者通过丰富的图表、源代码示例和实际应用案例，引导读者逐步理解并掌握深度学习技术在数据处理、机器学习以及自然语言处理领域的应用。第1章“深度学习概述”首先介绍了人工智能的发展历程，区分了机器与人类思维的不同之处，并阐述了深度学习作为AI的重要分支。这一章节概述了深度学习的基本概念，帮助读者建立对这个领域整体认知的基础。第二章“算法准备：迈向深度学习”从入门开始，强调了机器学习训练的重要性，区分了监督学习和无监督学习这两种主要的学习方式。此外，还详细讲解了神经网络理论及其背后的算法原理，为后续的深度学习实践打下坚实基础。第三章“深度信念网络与堆叠自编码器”深入探讨了神经网络的历史演变，展示了深度学习算法如何弥补传统神经网络的不足。这部分内容着重介绍深度信念网络（DBNs）和堆叠自编码器，让读者理解这些深度学习模型的工作原理。第四章“Dropout与卷积神经网络”转向深度学习算法的实践，不再依赖预训练，介绍了Dropout技术，这是一种防止过拟合的有效手段。接着，章节介绍了卷积神经网络（CNN），这是深度学习在图像识别和计算机视觉中的核心应用。最后，第五章“探索Java深度学习库：DL4J、ND4J及其他”是本书的核心部分，详细讲解如何使用Java深度学习库，如DL4J和ND4J等工具进行实际项目开发。章节内容涵盖了从零开始构建神经网络模型的过程，以及如何利用这些库来解决实际问题。《深度学习 - Java实践神经网络》不仅提供了理论知识，更注重实践操作，旨在帮助读者将理论知识转化为实际解决问题的能力，解决现实世界中的复杂问题，将神经网络理论与实际应用紧密联系起来。"

Making a machine gain knowledge is not like a human ordering a machine what

to do one-sidedly, but more like a machine being able to respond to what

humans ask and then answer. One of the simple examples of how this is

applied to the actual world is positive-negative analysis, one of the topics of

sentiment analysis. If you input data that defines a tone of positive or negative

for every word in a sentence (called "a dictionary") into a machine

beforehand, a machine can compare the sentence and the dictionary to find out

whether the sentence is positive or negative.

This technique is used for the positive-negative analysis of posts or comments

on a social network or blog. If you ask a machine "Is the reaction to this blog

post positive or negative?" it analyzes the comments based on its knowledge

(dictionary) and replies to you. From the first AI boom, where a machine only

followed rules that humans set, the second AI boom showed some progress.

By integrating knowledge into a machine, a machine becomes the almighty.

This idea itself is not bad for achieving AI; however, there were two high

walls ahead of us in achieving it. First, as you may have noticed, inputting all

number of people who talked about AI decreased. When it came to the question

of "Are we really able to achieve AI?" the number of people who answered

"no" increased gradually.

Machine learning evolves

While people had a hard time trying to establish a method to achieve AI, a

completely different approach had steadily built a generic technology . That

approach is called machine learning. You should have heard the name if you

have touched on data mining even a little. Machine learning is a strong tool

compared to past AI approaches, which simply searched or assumed based on

the knowledge given by a human, as mentioned earlier in the chapter, so

machine learning is very advanced. Until machine learning, a machine could

only search for an answer from the data that had already been inputted. The

focus was on how fast a machine could pull out knowledge related to a

question from its saved knowledge. Hence, a machine can quickly reply to a

question it already knows, but gets stuck when it faces questions it doesn't

know.

On the other hand, in machine learning, a machine is literally learning. A

machine can cope with unknown questions based on the knowledge it has

learned. So, how was a machine able to learn, you ask? What exactly is

learning here? Simply put, learning is when a machine can divide a problem

into "yes" or "no." We'll go through more detail on this in the next chapter, but

for now we can say that machine learning is a method of pattern recognition.

We could say that, ultimately, every question in the world can be replaced with

a question that can be answered with yes or no. For example, the question

"What color do you like?" can be considered almost the same as asking "Do

you like red? Do you like green? Do you like blue? Do you like yellow?..." In

machine learning, using the ability to calculate and the capacity to process at

high speed as a weapon, a machine utilizes a substantial amount of training

data, replaces complex questions with yes/no questions, and finds out the

regularity with which data is yes, and which data is no (in other words, it

learns). Then, with that learning, a machine assumes whether the newly-given

data is yes or no and provides an answer. To sum up, machine learning can

give an answer by recognizing and sorting out patterns from the data provided

and then classifying that data into the possible appropriate pattern (predicting)

when it faces unknown data as a question.

In fact, this approach is not doing something especially difficult. Humans also

unconsciously classify data into patterns. For example, if you meet a

man/woman who's perfectly your type at a party, you might be desperate to

know whether the man/woman in front of you has similar feelings towards you.

In your head, you would compare his/her way of talking, looks, expressions, or

gestures to past experience (that is, data) and assume whether you will go on a

date! This is the same as a presumption based on pattern recognition.

Machine learning is a method that can process this pattern recognition not by

humans but by a machine in a mechanical manner. So, how can a machine

recognize patterns and classify them? The standard of classification by

machine learning is a presumption based on a numerical formula called the

probabilistic statistical model. This approach has been studied based on

various mathematical models.

Learning, in other words, is tuning the parameters of a model and, once the

learning is done, building a model with one adjusted parameter. The machine

then categorizes unknown data into the most possible pattern (that is, the

pattern that fits best). Categorizing data mathematically has great merit. While

it is almost impossible for a human to process multi-dimensional data or

multiple-patterned data, machine learning can process the categorization with

almost the same numerical formulas. A machine just needs to add a vector or

the number of dimensions of a matrix. (Internally, when it classifies multi-

dimensions, it's not done by a classified line or a classified curve but by a

hyperplane.)

Until this approach was developed, machines were helpless in terms of

responding to unknown data without a human's help, but with machine learning

machines became capable of responding to data that humans can't process.

Researchers were excited about the possibilities of machine learning and

jumped on the opportunity to start working on improving the method. The

concept of machine learning itself has a long history, but researchers couldn't

do much research and prove the usefulness of machine learning due to a lack of

available data. Recently, however, many open-source data have become

available online and researchers can easily experiment with their algorithms

using the data. Then, the third AI boom came about like this. The environment

surrounding machine learning also gave its progress a boost. Machine learning

needs a massive amount of data before it can correctly recognize patterns. In

addition, it needs to have the capability to process data. The more data and

types of patterns it handles, the more the amount of data and the number of

calculations increases. Hence, obviously, past technology wouldn't have been

able to deal with machine learning.

However, time is progressing, not to mention that the processing capability of

machines has improved. In addition, the web has developed and the Internet is

spreading all over the world, so open data has increased. With this

development, everyone can handle data mining only if they pull data from the

web. The environment is set for everyone to casually study machine learning.

The web is a treasure box of text-data. By making good use of this text-data in

the field of machine learning, we are seeing great development, especially

with statistical natural language processing. Machine learning has also made

outstanding achievements in the field of image recognition and voice

recognition, and researchers have been working on finding the method with the

best precision.

Machine learning is utilized in various parts of the business world as well. In

the field of natural language processing, the prediction conversion in the input

method editor (IME) could soon be on your mind. The fields of image

recognition, voice recognition, image search, and voice search in the search

engine are good examples. Of course, it's not limited to these fields. It is also

applied to a wide range of fields from marketing targeting, such as the sales

prediction of specific products or the optimization of advertisements, or

designing store shelf or space planning based on predicting human behavior, to

predicting the movements of the financial market. It can be said that the most

used method of data mining in the business world is now machine learning.

Yes, machine learning is that powerful. At present, if you hear the word "AI,"

it's usually the case that the word simply indicates a process done by machine

learning.

What even machine learning cannot do

A machine learns by gathering data and predicting an answer. Indeed, machine

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