Deep learning (DL), in a nutshell, is a machine learning technique that teaches computers to do what comes naturally to most humans: what we consider learning by example. DL is considered a key technology behind new technology such as driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a mailbox. It also the key to voice control in consumer electronics such as phones, tablets, TVs, and hands-free speakers. DL gets a lot of attention recently and for good reason. It’s achieving results that were not possible using other means before.
DL can also be said to be an artificial intelligence function that copies the workings of the human brain in processing data and creating patterns that can be used in general decision making. Deep learning one part of machine learning in AI that has networks with the ability to learn unsupervised from data that is unstructured or unlabeled. It is also called deep neural learning or deep neural network.
When it comes to deep learning, a computer model is set to learn to perform classification tasks directly from text, images, or sound. DL models can achieve state-of-the-art accuracy and efficiency, sometimes exceeding normal human-level performance. Models for DL are trained using a large set of labeled data and neural network (NN) architectures that contain many layers, with more complexity to the setup as required.
Deep learning has come a long way, it has evolved hand-in-hand with the digital era, this has brought about an explosion of data in all forms and from every sector of our day to day lives. This collection of data, known simply as big data, is drawn from sources such as forums, social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible for use and can be shared across various applications through fintech applications like cloud computing.
But this data, which normally is unstructured, can be so vast that it could take decades for humans to comprehend it and extract relevant information. Companies and various organizations now realize the incredible potential that can result from unraveling this mountain of information and are increasingly adapting to AI systems for automated support and key operational functions.
We mentioned earlier that Deep learning is a part of the larger machine learning architecture which utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. These artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While standard programs build analysis with data in a linear or single directional way/format, the hierarchical function of DL systems enables devices to process data with a nonlinear or even multi-directional approach.
To use a single word, accuracy. Deep learning achieves recognition accuracy at a higher rate or level than what was previously possible. This helps consumer devices meet user expectations, and it is crucial for mission-critical and safety-focused applications like driverless cars. Recent progress in deep learning has improved to the point where deep learning outperforms humans in some tasks like classifying objects in images, a task which normally would take humans a long period of time.
While deep learning was first theorized in the 1980s, there are two main factors as to why it has only recently begun gaining momentum:
- Deep learning requires large amounts of labeled data, when we say large we are talking about data sets that would be too much to humans to even fathom. For example, driverless car development requires millions of images and thousands of hours of video and yet all these need to be processed in real-time.
- Deep learning requires a great amount of computing power. Newer High-performance GPUs have a parallel architecture that more efficient towards deep learning algorithms. When combined with clusters or cloud computing infrastructures, this enables development teams to reduce training time for a deep learning neural network from weeks to hours or less.
So what's the Difference Between Machine Learning (ML) and Deep Learning (DL)?
Deep learning is just a specialized form of machine learning when we get down to it. A machine learning workflow begins when we have relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning algorithm, the required features are automatically taken out of images. In addition, DL also performs “end-to-end learning” – where a network is given raw or unstructured data and a task to perform, such as classification, and it then learns how this can be done automatically.
Another key difference is the deep learning algorithms scale with data, whereas shallow learning converges. When we say Shallow learning, we are referring to machine learning methods that plateau or can progress no further at a certain level of performance when you add more examples and training data to the network.
The plus side here for deep learning networks is that they often continue to improve as the size of your data increases or more information is feed into it.
Deep learning is part of our everyday lives now, and this will only increase with time. Regardless if you are thinking about cars that drive themselves or even have some new technology such as parking assistance, traffic control or face recognition technology at various ports. Reports on events are likewise written by computers, more precisely: Computers using deep learning technologies to remain a constant learning process.
Machine learning enables communication between computers and humans. This includes our ability to “talk” with cars, identify dangerous hot zones or report on varying subject matters. Not only will their function and capability increase over time, but it will also impact our economy and most industries as well as the jobs that come with them. Therefore, it is helpful for employers and managers likewise to learn about its functions, development, and implementations, as well as how it is evolving and where it is heading. Eventually, it will impact all of us on a daily basis, an excellent example of this already exists in the smartphones we carry around.
There are a few jargon and concepts one needs to be familiar with when it comes to deep learning.
Logistic Regression – The regression analysis identifies a connection between input variables to predict outcome variables. It a simplified classification algorithm for learning to make decisions between predicting different variables. Take something as simple as handwriting as an example: whether it is an I or L, etc.
Activation Function – Nonlinear activation functions are applied to layers and allow Neural Networks to identify complex decision boundaries.
Artificial Neural Network – Input data is taken in, transformed and applied. The repetition of steps allows the artificial neural network to learn several layers of non-linear features and ultimately creates a prediction as to the final layer (output). It learns by creating an error signal measuring the differences between predictions.
Layer – Deep learning consists of building blocks, and layers are the highest level building blocks bordered by input, and output layers are the hidden layers. The received weighed input is transformed and then passed on as output to the next layer.
Artificial Neuron or Unit – This refers to the activation function and contains numerous incoming and outcoming connections. More complex units are referred to as long short term memory units.
To develop or train a deep network from the ground up, you first need to gather a very large labeled data set and design a network architecture that will learn the features and model. This is desired for new applications or applications that will have a large number of output fields. But this is a less common approach because, with a large amount of data and rate of learning, these networks typically take days or weeks to train.
Most deep learning algorithms use the transfer learning approach, a process that involves fine-tuning a pre-defined model. You begin with an existing network, such as AlexNet or GoogLeNet or any open set available online, and feed in new data containing previously unknown classes. After making some modifications or adjustments to the network, you can then perform a new task, such as filtering only dogs or cats instead of 1000 different objects. This also has the advantage of using much less data (processing thousands of images, rather than millions), so computation time drops drastically.
Transfer learning requires a connection to the internals of the pre-defined network, so it can be surgically modified and enhanced for the new task. MATLAB®, for example, has the tools and functions designed to help you do transfer learning.
What we can expect from the use of Deep Learning technology is largely based on our needs and the extent of our imagination. But it is without a doubt that DL is and will continue to be an integral part of modern technological data sorting and processing, be it in our smart cards, digital assistant, smart homes or web searches. The human race produces more information than it can possibly process without integrating tools like Artifical intelligence through machine learning and deep learning algorithms.
Here are a few reading references to get you started on your path to understanding the deeper intricacies of Deep Learning:
1. Goodfellow, Bengio, and Courville’s Deep Learning Book: http://www.deeplearningbook.org/
2. Neural Networks and Deep Learning by Michael Nielsen: http://neuralnetworksanddeeplearning.com/
3. Francois Chollet's Deep Learning with Python: https://amzn.to/2nkL59m
5. Aurélien Géron’s Hands-on Machine Learning with Scikit-Learn and TensorFlow: https://amzn.to/2mBFc7u
6: Gulli and Kapoor’s TensorFlow Deep Learning Cookbook: https://amzn.to/2o3QBNK