Book Image

Java Deep Learning Essentials

By : Yusuke Sugomori
Book Image

Java Deep Learning Essentials

By: Yusuke Sugomori

Overview of this book

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It’s something that’s moving beyond the realm of data science – if you’re a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you’ve got to grips with the fundamental mathematical principles, you’ll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you’ll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today. By the end of the book, you’ll be ready to tackle Deep Learning with Java. Wherever you’ve come from – whether you’re a data scientist or Java developer – you will become a part of the Deep Learning revolution!
Table of Contents (15 chapters)
Java Deep Learning Essentials
About the Author
About the Reviewers
Other Important Deep Learning Libraries

Transition of AI

So, why is it now that deep learning is in the spotlight? You might raise this question, especially if you are familiar with machine learning, because deep learning is not that different to any other machine learning algorithm (don't worry if you don't know this, as we'll go through it later in the book). In fact, we can say that deep learning is the adaptation of neural networks, one of the algorithms of machine learning, which mimics the structure of a human brain. However, what deep learning can achieve is much more significant and different to any other machine learning algorithm, including neural networks. If you see what processes and research deep learning has gone through, you will have a better understanding of deep learning itself. So, let's go through the transition of AI. You can just skim through this while sipping your coffee.

Definition of AI

All of a sudden, AI has become a hot topic in the world; however, as it turns out, actual AI doesn't exist yet. Of course, research is making progress in creating actual AI, but it will take more time to achieve it. Pleased or not, the human brain—which could be called "intelligence"—is structured in an extremely complicated way and you can't easily replicate it.

But wait a moment - we see many advertisements for products with the phrase by AI or using AI all over them. Are they fraudulent? Actually, they are! Surprised? You might see words like recommendation system by AI or products driven by AI, but the word AI used here doesn't indicate the actual meaning of AI. Strictly speaking, the word AI is used with a much broader meaning. The research into AI and the AI techniques accumulated in the past have achieved only some parts of AI, but now people are using the word AI for those parts too.

Let's look at a few examples. Roughly divided, there are three different categories recognized as AI in general:

  • Simple repetitive machine movements that a human programmed beforehand. For example, high speed processing industrial robots that only process the same set of work.

  • Searching or guessing answers to a given assignment following rules set by a human. For example, the iRobot Roomba can clean up along the shape of a room as it can assume the shape of a room by bumping into obstacles.

  • Providing an answer to unknown data by finding measurable regularity from the existing data. For example, a product recommendation system based on a user's purchase history or distributing banner ads among ad networks falls under this category.

People use the word AI for these categories and, needless to say, new technology that utilizes deep learning is also called AI. Yet, these technologies are different both in structure and in what they can do. So, which should we specifically call AI? Unfortunately, people have different opinions about that question and the answer cannot be objectively explained. Academically, a term has been set as either strong AI or weak AI depending on the level that a machine can achieve. However, in this book, to avoid confusion, AI is used to mean (Not yet achieved) human-like intelligence that is hard to distinguish from the actual human brain. The field of AI is being drastically developed, and the possibility of AI becoming reality is exponentially higher when driven by deep learning. This field is booming now more than ever in history. How long this boom will continue depends on future research.

AI booms in the past

AI suddenly became a hot topic recently: however, this is not the first AI boom. When you look back to the past, research into AI has been conducted for decades and there has been a cycle of being active and inactive. The recent boom is the third boom. Therefore, some people actually think that, at this time, it's just an evanescent boom again.

However, the latest boom has a significant difference from the past booms. Yes, that is deep learning. Deep learning has achieved what the past techniques could not achieve. What is that? Simply put, a machine itself is able to find out the feature quantity from the given data, and learn. With this achievement, we can see the great possibility of AI becoming a reality, because until now a machine couldn't understand a new concept by itself and a human needed to input a certain feature quantity in advance using past techniques created in the AI field.

It doesn't look like a huge difference if you just read this fact, but there's a world of difference. There has been a long path taken before reaching the stage where a machine can measure feature quantity by itself. People were finally able to take a big step forward when a machine could obtain intelligence driven by deep learning. So, what's the big difference between the past techniques and deep learning? Let's briefly look back into the past AI field to get a better sense of the difference.

The first AI boom came in the late 1950s. Back then, the mainstream research and development of a search program was based on fixed rules—needless to say, they were human-defined. The search was, simply put, dividing cases. In this search, if we wanted a machine to perform any process, we had to write out every possible pattern we might need for the process. A machine can calculate much faster than a human can. It doesn't matter how enormous the patterns are, a machine can easily handle them. A machine will keep searching a million times and eventually will find the best answer. However, even if a machine can calculate at high speed, if it is just searching for an answer randomly and blindly it will take a massive amount of time. Yes, don't forget that constraint condition, "time." Therefore, further studies were conducted on how to make the search more efficient. The most popular search methods among the studies were depth-first search (DFS) and breadth-first search (BFS).

Out of every possible pattern you can think of, search for the most efficient path and make the best possible choice among them within a realistic time frame. By doing this, you should get the best answer each time. Based on this hypothesis, two searching or traversing algorithms for a tree of graph data structures were developed: DFS and BFS. Both start at the root of a graph or tree, and DFS explores as far as possible along each branch before backtracking, whereas BFS explores the neighbor nodes first before moving to the next level neighbors. Here are some example diagrams that show the difference between DFS and BFS:

These search algorithms could achieve certain results in a specific field, especially fields like Chess and Shogi. This board game field is one of the areas that a machine excels in. If it is given an input of massive amounts of win/lose patterns, past game data, and all the permitted moves of a piece in advance, a machine can evaluate the board position and decide the best possible next move from a very large range of patterns.

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:

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 real-world knowledge requires an almost infinite amount of work now that the Internet is more commonly used and we can obtain enormous amounts of open data from the Web. Back then, it wasn't realistic to collect millions of pieces of data and then analyze and input that knowledge into a machine. Actually, this work of databasing all the world's data has continued and is known as Cyc ( Cyc's ultimate purpose is to build an inference engine based on the database of this knowledge, called knowledge base. Here is an example of KR using the Cyc project:

Second, it's not that a machine understands the actual meaning of the knowledge. Even if the knowledge is structured and systemized, a machine understands it as a mark and never understands the concept. After all, the knowledge is 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.