Java深度学习实战：探索DL4J、Theano与Caffe

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"Java深度学习书籍，由Yusuke Sugomori撰写，Packt Publishing于2016年出版，ISBN：9781785282195，涵盖主题包括数据分析。本书旨在深入浅出地介绍数据科学的未来，并教授如何使用Java构建深度学习和人工智能的核心算法。适合于数据科学家、Java开发者以及想要利用深度学习进行项目开发的机器学习用户阅读。" 在《Java Deep Learning Essentials》这本书中，作者Yusuke Sugomori引领读者超越理论，将深度学习付诸实践。本书重点在于通过Java来实现深度学习，涵盖了多种领先框架，如DL4J、Theano和Caffe。无论你是数据科学家还是Java开发者，甚至是希望在大数据环境中应用深度学习的机器学习用户，都能从中受益。本书中，读者将学到以下内容： 1. 进行深入的机器学习和深度学习算法实践。这不仅包括了理论知识的讲解，更注重实际操作，让读者能够动手实现这些复杂的算法。 2. 实现与深度学习相关的机器学习算法。深度学习是机器学习的一个分支，书中将介绍如何在Java环境下构建这些算法，以解决实际问题。 3. 探索使用流行深度学习框架构建的神经网络。DL4J、Theano和Caffe等框架提供了构建和训练神经网络的强大工具，读者将了解到如何运用这些框架来搭建和优化模型。 4. 学习如何运用深度学习技术处理大数据环境中的问题。随着大数据时代的到来，深度学习在处理大规模数据集时的优势越来越明显，本书将指导读者在这样的环境中有效应用深度学习。 5. 逐步指导，从基础知识到高级技巧，帮助读者逐步建立深度学习项目。这将涉及数据预处理、模型训练、验证和评估，以及模型的部署和维护。 6. 了解深度学习的实际应用案例。通过具体的项目实例，读者可以更好地理解深度学习在图像识别、自然语言处理、推荐系统等领域中的应用。《Java Deep Learning Essentials》是一本面向实践者的深度学习指南，它将帮助读者掌握深度学习的关键概念和工具，提升在Java开发中的数据科学能力。对于希望在Java环境中开展深度学习工作的专业人士来说，这是一本不可多得的参考资料。

For those of you who are interested in this field, let's look into how a machine plays chess in more detail. Let's

say a machine makes the first move as "white," and there are 20 possible moves for both "white" and "black" for

the next move. Remember the tree-like model in the preceding diagram. From the top of the tree at the start of the

game, there are 20 branches underneath as white's next possible move. Under one of these 20 branches, there's another

20 branches underneath as black's next possible movement, and so on. In this case, the tree has 20 x 20 = 400 branches

for black, depending on how white moves, 400 x 20 = 8,000 branches for white, 8,000 x 20 = 160,000 branches again

for black, and... feel free to calculate this if you like.

A machine generates this tree and evaluates every possible board position from these branches, deciding the best

arrangement in a second. How deep it goes (how many levels of the tree it generates and evaluates) is controlled

by the speed of the machine. Of course, each different piece's movement should also be considered and embedded in

a program, so the chess program is not as simple as previously thought, but we won't go into detail about this in

this book. As you can see, it's not surprising that a machine can beat a human at Chess. A machine can evaluate

and calculate massive amounts of patterns at the same time, in a much shorter time than a human could. It's not

a new story that a machine has beaten a Chess champion; a machine has won a game over a human. Because of stories

like this, people expected that AI would become a true story.

Unfortunately, reality is not that easy. We then found out that there was a big wall in front of us preventing us

from applying the search algorithm to reality. Reality is, as you know, complicated. A machine is good at processing

things at high speed based on a given set of rules, but it cannot find out how to act and what rules to apply by

itself when only a task is given. Humans unconsciously evaluate, discard many things/options that are not related

to them, and make a choice from millions of things (patterns) in the real world whenever they act. A machine cannot

make these unconscious decisions like humans can. If we create a machine that can appropriately consider a phenomenon

that happens in the real world, we can assume two possibilities:

 A machine tries to accomplish its task or purpose without taking into account secondarily occurring incidents and possibilities

 A machine tries to accomplish its task or purpose without taking into account irrelevant incidents and possibilities

Both of these machines would still freeze and be lost in processing before they accomplished their purpose when

humans give them a task; in particular, the latter machine would immediately freeze before even taking its first

action. This is because these elements are almost infinite and a machine can't sort them out within a realistic

time if it tries to think/search these infinite patterns. This issue is recognized as one of the important challenges

in the AI field, and it's called the frame problem.

A machine can achieve great success in the field of Chess or Shogi because the searching space, the space a machine

should be processing within, is limited (set in a certain frame) in advance. You can't write out an enormous amount

of patterns, so you can't define what the best solution is. Even if you are forced to limit the number of patterns

or to define an optimal solution, you can't get the result within an economical time frame for use due to the enormous

amounts of calculation needed. After all, the research at that time would only make a machine follow detailed rules

set by a human. As such, although this search method could succeed in a specific area, it is far from achieving

actual AI. Therefore, the first AI boom cooled down rapidly with disappointment.

The first AI boom was swept away; however, on the side, the research into AI continued. The second AI boom came

in the 1980s. This time, the movement of so-called Knowledge Representation (KR) was booming. KR intended to describe

knowledge that a machine could easily understand. If all the knowledge in the world was integrated into a machine

and a machine could understand this knowledge, it should be able to provide the right answer even if it is given

a complex task. Based on this assumption, various methods were developed for designing knowledge for a machine to

understand better. For example, the structured forms on a web page—the semantic web—is one example of an approach

that tried to design in order for a machine to understand information easier. An example of how the semantic web

is described with KR is shown here:

input by a human and what a machine does is just compare the data and assume meaning based on the dictionary. For

example, if you know the concept of "apple" and "green" and are taught "green apple = apple + green", then you can

understand that "a green apple is a green colored apple" at first sight, whereas a machine can't. This is called

the symbol grounding problem and is considered one of the biggest problems in the AI field, as well as the frame

problem.

The idea was not bad—it did improve AI—however, this approach won't achieve AI in reality as it's not able to

create AI. Thus, the second AI boom cooled down imperceptibly, and with a loss of expectation from AI, the 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 learning is very useful. Thanks to

machine learning, questions that are difficult for a human to solve within a realistic time frame (such as using

a 100-dimensional hyperplane for categorization!) are easy for a machine. Recently, "big data" has been used as

a buzzword and, by the way, analyzing this big data is mainly done using machine learning too.

Unfortunately, however, even machine learning cannot make AI. From the perspective of "can it actually achieve AI?"

machine learning has a big weak point. There is one big difference in the process of learning between machine learning

and human learning. You might have noticed the difference, but let's see. Machine learning is the technique of pattern

classification and prediction based on input data. If so, what exactly is that input data? Can it use any data?

Of course… it can't. It's obvious that it can't correctly predict based on irrelevant data. For a machine to learn

correctly, it needs to have appropriate data, but then a problem occurs. A machine is not able to sort out what

is appropriate data and what is not. Only if it has the right data can machine learning find a pattern. No matter

how easy or difficult a question is, it's humans that need to find the right data.

Let's think about this question: "Is the object in front of you a human or a cat?" For a human, the answer is all

too obvious. It's not difficult at all to distinguish them. Now, let's do the same thing with machine learning.

First, we need to prepare the format that a machine can read, in other words, we need to prepare the image data

of a human and a cat respectively. This isn't anything special. The problem is the next step. You probably just

want to use the image data for inputting, but this doesn't work. As mentioned earlier, a machine can't find out

what to learn from data by itself. Things a machine should learn need to be processed from the original image data

and created by a human. Let's say, in this example, we might need to use data that can define the differences such

as face colors, facial part position, the facial outlines of a human and a cat, and so on, as input data. These

values, given as inputs that humans need to find out, are called the features.

Machine learning can't do feature engineering. This is the weakest point of machine learning. Features are, namely,

variables in the model of machine learning. As this value shows the feature of the object quantitatively, a machine

can appropriately handle pattern recognition. In other words, how you set the value of identities will make a huge

difference in terms of the precision of prediction. Potentially, there are two types of limitations with machine

learning:

 An algorithm can only work well on data with the assumption of the training data - with data that has different distribution. In many cases, the

learned model does not generalize well.

 Even the well-trained model lacks the ability to make a smart meta-decision. Therefore, in most cases, machine learning can be very successful in a

very narrow direction.

Let's look at a simple example so that you can easily imagine how identities have a big influence on the prediction

precision of a model. Imagine there is a corporation that wants to promote a package of asset management based on

the amount of assets. The corporation would like to recommend an appropriate product, but as it can't ask a personal

question, it needs to predict how many assets a customer might have and prepare in advance. In this case, what type

of potential customers shall we consider as an identity? We can assume many factors such as their height, weight,

age, address, and so on as an identity, but clearly age or residence seem more relevant than height or weight. You

probably won't get a good result if you try machine learning based on height or weight, as it predicts based on

irrelevant data, meaning it's just a random prediction.

As such, machine learning can provide an appropriate answer against the question only after the machine reads an

appropriate identity. But, unfortunately, the machine can't judge what the appropriate identity is, and the precision

of machine learning depends on this feature engineering!

Machine learning has various methods, but the problem of being unable to do feature engineering is seen across all

of these. Various methods have been developed and people compete against their precision rates, but after we have

achieved precision to a certain extent, people decide whether a method of machine learning is good or bad based

on how great a feature they can find. This is no longer a difference in algorithms, but more like a human's intuition

or taste, or the fine-tuning of parameters, and this can't be said to be innovative at all. Various methods have

been developed, but after all, the hardest thing is to think of the best identity and a human has to do that part

anyway.

Things dividing a machine and human

We have gone through three problems: the frame problem, the symbol grounding problem, and feature engineering. None

of these problems concern humans at all. So, why can't a machine handle these problems? Let's review the three problems

again. If you think about it carefully, you will find that all three problems confront the same issue in the end:

 The frame problem is that a machine can't recognize what knowledge it should use when it is assigned a task

 The symbol grounding problem is that a machine can't understand a concept that puts knowledge together because

it only recognizes knowledge as a mark

 The problem of feature engineering in machine learning is that a machine can't find out what the feature is

for objects

These problems can be solved only if a machine can sort out

which feature of things/phenomena it should focus on

and what information it should use

. After all, this is the biggest difference between a machine and a human. Every

object in this world has its own inherent features. A human is good at catching these features. Is this by experience

or by instinct? Anyhow, humans know features, and, based on these features, humans can understand a thing as a

"concept."

Now, let's briefly explain what a concept is. First of all, as a premise, take into account that every single thing

in this world is constituted of a set of symbol representations and the symbols' content. For example, if you don't

know the word "cat" and see a cat when you walk down a street, does it mean you can't recognize a cat? No, this

is not true. You know it exists, and if you see another cat just after, you will understand it as "a similar thing

to what I saw earlier." Later, you are told "That is called a cat", or you look it up for yourself, and for the

first time you can connect the existence and the word.

This word, cat, is the symbol representation and the concept that you recognize as a cat is the symbol content.

You can see these are two sides of the same coin. (Interestingly, there is no necessity between these two sides.

There is no necessity to write cat as C-A-T or to pronounce it as such. Even so, in our system of understanding,

these are considered to be inevitable. If people hear "cat", we all imagine the same thing.) The concept is, namely,

symbol content. These two concepts have terms. The former is called signifiant and the latter is called signifié,

and a set of these two as a pair is called signe. (These words are French. You can say signifier, signified, and

itself or not.

What would happen if a machine could find the notable feature from given data? As for the frame problem, if a machine

could extract the notable feature from the given data and perform the knowledge representation, it wouldn't have

the problem of freezing when thinking of how to pick up the necessary knowledge anymore. In terms of the symbol

grounding problem, if a machine could find the feature by itself and understand the concept from the feature, it

could understand the inputted symbol.

Needless to say, the feature engineering problem in machine learning would also be solved. If a machine can obtain

appropriate knowledge by itself following a situation or a purpose, and not use knowledge from a fixed situation,

we can solve the various problems we have been facing in achieving AI. Now, the method that a machine can use to

find the important feature value from the given data is close to being accomplished. Yes, finally, this is deep

learning. In the next section, I'll explain this deep learning, which is considered to be the biggest breakthrough

in the more-than-50 years of AI history.

AI and deep learning

Machine learning, the spark for the third AI boom, is very useful and powerful as a data mining method; however,

even with this approach of machine learning, it appeared that the way towards achieving AI was closed. Finding features

is a human's role, and here there is a big wall preventing machine learning from reaching AI. It looked like the

third AI boom would come to an end as well. However, surprisingly enough, the boom never ended, and on the contrary

a new wave has risen. What triggered this wave is deep learning.

With the advent of deep learning, at least in the fields of image recognition and voice recognition, a machine became

able to obtain "what should it decide to be a feature value" from the inputted data by itself rather than from a

human. A machine that could only handle a symbol as a symbol notation has become able to obtain concepts.

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