Source: Deep Learning on Medium
I am starting this blog to share my understanding of this amazing book Deep Learning that is written by Ian Goodfellow, Yoshua Bengio and Aaron Cournville. I just started reading this book and thought it will be more fun if I share what I will learn and understand throughout the journey of this book. I will try to write a brief and compact form of this book chapter by chapter, so this blog will be a series of blogs about this book.
Before I will jump into our first chapter let me tell you all about this book a little. For those who don’t know, this is like the holy bible for the deep learning enthusiast peoples. Those who wants a detailed mathematical introduction into the world of deep learning must read this book. It is written by pioneers of this field and it is also available free on deeplearning.org.
Now let’s get started
What is AI? What is all this fuss about? So let’s start with the formal definition:
It is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction.
But if you want to define AI in some informal and easy language then:
It is the phenomenon or task in which we try to create machines which can imitate humans during work i.e. their intelligence and logic.
This is not something that started some years ago, inventors have long dreamed about achieving this at least from the time of ancient Greece. Today there are many fields where you can see the applications of artificial intelligence (AI) like intelligent software to automate routine labour, facial recognition and speech recognition on smart phones, in medical diagnoses and scientific research etc.
Previously several of the AI projects have sought to be hard coded knowledge using logical inference rules and this is known as knowledge base approach to artificial intelligence. But sadly none of those projects led to major success because it is impossible to a human operator to define all the rules manually for the machine or to find out all the possible cases for a particular work because it can be anything in case of real world.
1.1 Machine Learning
These difficulties that were faced by ancient AI systems suggest the need of ability to acquire their own knowledge, by extracting patterns from the raw data. This capability is known as Machine Learning.
Machine learning enabled computers fed on real life raw data or examples and it tries to extract patterns from it and make better decisions by itself. Some of the machine learning algorithms are logistic regression, naive bayes, SVM etc.
The performance of these machine learning algorithms depends heavily on the representation of the data that are given. Each piece of information included in the representation is known as features and these algorithms learns how to use these features to extract patterns or to get knowledge.
But sometimes it’s difficult to extract know what features should be extracted. For example suppose we wants to detect cars from an image, now we might like to use the presence of wheel as a feature. But it is difficult to describe what a wheel looks like in terms of pixel values. One solution to this problem is to use machine learning to discover not only output from those features(representation) but the features itself. This approach is known as representation learning. Again it is much better if algorithm learns features by itself with minimal human intervention.
While designing these algorithms our goal is usually to separate the factors of variation. Now these factors are not always directly observed they might be unobserved factors also that can affect our algorithm. Now of course it can be difficult to extract high-level, abstract features from raw data like speaker’s accent in case of voice recognition because these can be identified by only sophisticated human level understanding of the data.
1.2 Deep Learning
Deep Learning can solve this problem in representation learning by introducing representation that are expressed in terms of other, simpler representation.
It’s the little details that are vital. Little things make big things happen. ~ John Wooden
I think above quote perfectly fits in the working style of deep learning, it enables to build complex concepts out of simpler concepts. To understand it more “deeply” let’s take an example:
Above figure is the illustration of a deep learning model. As I have mentioned earlier that it is difficult for an algorithm to understand the raw input data by itself, so deep learning tackled this problem by breaking the input(mappings) into simpler form which is described by each layer of the model.
As you can see in the above figure there are five layers and they all are interconnected to each other. If we categorise these layers then in above figure we have one input layer, one output layer and three hidden layers(these can be varied). The input is presented at the visible layer(input layer) and we are able to observe it, that explains the name. Then a series of hidden layers(this data is not given that’s why called hidden) extract abstract features increasingly. We will go into that “increasingly” part later. And then finally we have output layer which represented the output of the model.
Now as for the part “increasingly”, hidden layers are responsible for extracting features and say the first hidden layer is responsible for identifying edges in the input image. Given this second layer can easily search for corners ad extended contours and similarly given second layer, the third layer can detect entire parts of specific objects and this also explains the interconnectivity of the layers.
2. Historical Trends
One of the fact that you should know that deep learning is not a new technology, it dates back to the 1940s. But it appears to be new, because it was relatively unpopular for several years and that’s why we will look into some of the historical facts and trends to understand it’s origin.
There have been three waves of development: deep learning known as cybernetics(1940s-1960s), deep learning known as connectionism(1980s-1990s) and the current wave under the name deep learning(2006-present).
The first wave cybernetics started with development of theories of biological learning and implementation of the first models such as the perceptron, enabling the training of single neuron. The second wave started with the connectionist approach with back-propagation to train a neural network with one or two hidden layers. The current and third wave started around 2006 (Hinton et. al., 2006; Bengio et al., 2007; Ranzato et al., 2007a) that we will discuss in the upcoming parts of this series in detail.
3. Why deep learning became popular now?
As I have mentioned earlier that deep learning dates back from 1940s but recently get popular and catches eyes of many researchers and engineers. But the question arises with this is why now? The answer of this question is hidden in the “hidden layers” of the deep learning model. You saw earlier that deep learning model (neural networks) contains layers and to solve a particular(big) problem it needs many layers which in turn needs larger dataset and to process larger dataset we need more processing power and few things more. Let discuss this in more detail:
3.1. Increasing dataset sizes:
Deep learning has applications since 1990s but at that many researchers refused to use it because to make it work perfectly and for better results one need a large dataset which can be fed to the network so that hidden layers can extract every abstract features from it. But this all started to change by increasing digitization of society, more and more activities takes place on computers, increase in computers connected together in networks and so on. The age of “Big Data” has made the implementation of deep learning much easier and effective.
3.2. Increasing model sizes:
Another key reason in the success of neural networks is growth by faster computers with larger memory. After having availability of larger datasets, another hurdle faced by researchers was how to process and store this amount of data. But with today’s faster CPUs and GPUs and also with larger memory we have resources to work on larger datasets.
3.3. Increasing accuracy and real-world impact:
Because of all these advancement in technologies, the researchers now able to perform experiments on a very large scale and day by day new algorithms, concepts and results are found. Deep learning improved its ability in providing more accurate results and because of this its use is also increasing in real life too. Since researchers are now able to achieve almost human like results in tasks like voice recognition, object detection, image recognition etc, many major IT companies now using deep learning for their real world products.
In summary, deep learning is an approach to machine learning that has drawn heavily on our knowledge of human brain, applied maths and statistics. In recent years deep learning has seen tremendous growth in its popularity and usefulness.
This concludes the first part of this blog series. I am currently working on the second part of this series in which we are going deep with some applied mathematics and linear algebra. I will try to complete it as soon as possible and update it’s link here. I hope you enjoyed this part and we will learn more in the upcoming part too.
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Cournville.