Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. Deep learning methods are becoming exponentially more important due to their demonstrated success at tackling complex learning problems. At the same time, increasing access to high-performance computing resources and state-of-the-art open-source libraries are making it more and more feasible for enterprises, small firms, and individuals to use these methods.
Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
What is Deep Learning?
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labelled data and neural network architectures that contain many layers.
A deep learning technology is based on artificial neural networks(ANNs). These ANNs constantly receive learning algorithms and continuously growing amounts of data to increase the efficiency of training processes. The larger data volumes are, the more efficient this process is. The training process is called «deep», because, with the time passing, a neural network covers a growing number of levels. The «deeper» this network penetrates, the higher its productivity is.
How Deep Learning Works?
A deep machine learning process consists of two main phases: Training and Inferring. You should think about the training phase as a process of labelling large amounts of data and determining their matching characteristics. The system compares these characteristics and memorizes them to make correct conclusions when it faces similar data next time.
A deep learning training process includes following stages:
- ANNs ask a set of binary false/true questions or.
- Extracting numerical values from data blocks.
- Classifying data according to the answers received.
- Labeling Data.
During the inferring phase, the deep learning AI makes conclusions and label new unexposed data using their previous knowledge.
Advantages of Deep Learning
In 2016, Grand View Research (GVR) estimated the global deep learning market in $272 million. Its significant part (20%) belonged to both aerospace and defense industries. From 2014, the deep learning market shows a continuous parabolic growth. GVR’s latest report states that this market will reach the value of $10.2 billion by the end of 2025. So what did cause such a remarkable market growth? The answer lies in the set of advantages provided by a deep learning technology.
Creating New Features
One of the main benefits of deep learning over various machine learning algorithms is its ability to generate new features from limited series of features located in the training dataset. Therefore, deep learning algorithms can create new tasks to solve current ones. What does it mean for data scientists working in technological startups?
Since deep learning can create features without a human intervention, data scientists can save much time on working with big data and relying on this technology. It allows them to use more complex sets of features in comparison with traditional machine learning software.
Due to its improved data processing models, deep learning generates actionable results when solving data science tasks. While machine learning works only with labeled data, deep learning supports unsupervised learning techniques that allow the system become smarter on its own. The capacity to determine the most important features allows deep learning to efficiently provide data scientists with concise and reliable analysis results.
Challenges of Deep Learning
Deep learning is an approach that models human abstract thinking (or at least represents an attempt to approach it) rather than using it. However, this technology has a set of significant disadvantages despite all its benefits.
Continuous Input Data Management
In deep learning, a training process is based on analysing large amounts of data. Although, fast-moving and streaming input data provides little time for ensuring an efficient training process. That is why data scientists have to adapt their deep learning algorithms in the way neural networks can handle large amounts of continuous input data.
Ensuring Conclusion Transparency
Another important disadvantage of deep learning software is that it is incapable of providing arguments why it has reached a certain conclusion. Unlike in case of traditional machine learning, you cannot follow an algorithm to find out why your system has decided that it is a cat on a picture, not a dog. To correct errors in DL algorithms, you have to revise the whole algorithm.
Deep learning is a quite resource-demanding technology. It requires more powerful GPUs, high-performance graphics processing units, large amounts of storage to train the models, etc. Furthermore, this technology needs more time to train in comparison with traditional machine learning.
Despite all its challenges, deep learning discovers new improved methods of unstructured big data analytics for those with the intention to use it. Indeed, businesses can gain significant benefits from using deep learning within their tasks of data processing. Though, the question is not whether this technology is useful, rather how companies can implement it in their projects to improve the way they process data.
Examples of Deep Learning at Work
Deep learning applications are used in industries from automated driving to medical devices.
Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
Aerospace and Defence: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells.
Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.
Electronics: Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.
What’s the Difference Between Machine Learning and Deep Learning?
Deep learning is a specialized form of machine learning. A machine learning workflow starts with 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 workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.
Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.
A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.
In machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction and modelling steps are automatic.
Future of Deep Learning
Deep learning is many things, but it isn’t simple.
Even if you’re a data scientist who has mastered the basics of artificial neural networks, you may need time to get up to speed on the intricacies of convolutional, recurrent, generative, and every other species of multi-layered deep learning algorithm. As deep learning innovations proliferate, there’s a risk this technology will grow too complex for average developers to grasp without intensive study.
By the end of this decade possibilities that the deep learning industry will have simplified its offerings considerably so that they’re comprehensible and useful to the average developer. The chief trends toward deep learning tool, platform, and solution simplification are as follows:
1.The deep learning industry will adopt a core set of standard tools
By the end of this decade, the deep learning community will converge on a core set of de facto tooling frameworks. Currently, deep learning professionals have a glut of tooling options, most of which are open source. The most popular include TensorFlow, BigDL, OpenDeep, Caffe, Theano, Torch, and MXNet.
2. Deep learning will gain native support within Spark
The Spark community will beef up the platform’s native deep learning capabilities in the next 12 to 24 months. Judging by the sessions at the recent Spark Summit, it would appear that the community is leaning toward stronger support for TensorFlow, at the very least, with BigDL, Caffe, and Torch also picking up adoption.
3. Deep learning will find a stable niche within the open analytics ecosystem
Most deep learning deployments already depend on Spark, Hadoop, Kafka, and other open source data analytics platforms. What’s becoming clear is that you can’t adequately train, manage, and deploy deep learning algorithms without the full suite of big data analytics capabilities provided by these other platforms. In particular, Spark is becoming an essential platform for scaling and accelerating deep learning algorithms built in various tools. As I noted in this recent article, many deep learning developers are using Spark clusters for such specialized pipeline tasks as hyperparameter optimization, fast in-memory data training, data cleansing, and preprocessing.
4. Deep learning tools will incorporate simplified programming frameworks for fast coding
The application developer community will insist on APIs and other programming abstractions for fast coding of the core algorithmic capabilities with fewer lines of code. Going forward, deep learning developers will adopt integrated, open, cloud-based development environments that provide access to a wide range of off-the-shelf and pluggable algorithm libraries. These will enable API-driven development of deep learning applications as composable containerized microservices. The tools will automate more deep learning development pipeline functions and present a notebook-oriented collaboration and sharing paradigm. As this trend intensifies, we’ll see more more headlines such as “Generative Adversarial Nets in 50 Lines of Code (PyTorch).”
5. Deep learning toolkits will support visual development of reusable components
Deep learning toolkits will incorporate modular capabilities for easy visual design, configuration, and training of new models from pre-existing building blocks. Many such reusable components will be sourced through “transfer learning” from prior projects that addressed similar use cases. Reuseable deep learning artifacts, incorporated into standard libraries and interfaces, will consist of feature representations, neural-node layerings, weights, training methods, learning rates, and other relevant features of prior models.
6. Deep learning tools will be embedded in every design surface
It’s not too soon to start envisioning “democratized deep learning.” Within the next five to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. Equally as important, user-friendly deep learning development capabilities will be embedded in generative design toolsused by artists, designers, architects, and creative people of all stripes who would never go near a neural network. Driving this will be a popular mania for deep learning-powered tools for image search, autotagging, photorealistic rendering, resolution enhancement, style transformation, fanciful figure inception, and music composition.
As the deep learning market advances toward mass adoption, it will follow in the footsteps of data visualization, business intelligence, and predictive analytics markets. All of them have moved their solutions toward self-service cloud-based delivery models that deliver fast value for users who don’t want to be distracted by the underlying technical complexities. That’s the way technology evolves.