Over the past few years, deep learning has become another trendy word1. It is mostly used in a business language when the conversation is about Machine Learning, Artificial Intelligence, Big Data, analytics, etc. Currently, it is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. Therefore I decided to write an article about deep learning startups, use cases and books.
Deep Learning was developed as a Machine Learning approach to deal with complex input-output mappings. Deep learning crunches more data than machine learning and that is the biggest difference. If you have a little bit of data, machine learning is a good choice, but if you have a lot of data, deep learning is a better choice for you. Deep learning algorithms do complicated things, like matrix multiplications. They also learn high-level features, so in the case of facial recognition, the algorithm will get the image pretty close to the RAW version in replication whereas machine learning’s images would be blurry. Another powerful feature is that it forms an end-to-end solution instead of breaking a problem and solution down into parts.
What is Deep Learning?
But what is Deep Learning exactly? Why has it become so popular? In simple words, Deep learning carries out the machine learning process using an artificial neural net that is composed of some levels in a hierarchy. For example, the network learns something simple at the initial level in the hierarchy and then sends this information to the next level. Next level takes this simple information, combines it into something that is a bit more complex and passes it on the third level. This process continues as each level in the hierarchy builds something more complex from the input it received from the previous level.
Taking the example of a dog, the initial level of a deep learning network might use differences in the light and dark areas of an image to learn where edges or lines are in a picture of a dog. The initial level passes this information about edges to the second level which combines the edges into simple shapes like a diagonal line or a right angle. The third level combines the simple shapes into more complex objects likes ovals or rectangles. The next level might combine the ovals and rectangles into paws and tails. The process continues until it reaches the top level in the hierarchy where the network has learned to identify dogs. While it was learning about dogs, the network also learned to identify all of the other animals it saw along with the dogs. It is a very good option to identify errors, in general, it is a very fast and efficient way to analyse the huge amount of information and save cost.
Deep learning use cases
Just like we mentioned, Deep learning startups successfully apply it to big data for knowledge discovery, knowledge application, and knowledge-based prediction. In other words, deep learning can be a powerful engine for producing actionable results. A good way to see all the potential of deep learning is looking at deep learning startups and see how big companies apply and use it.
Let’s start with the most known examples; Deep Learning is heavily used by Google in its voice and image recognition algorithms1. Also, it is used by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.
How big companies and deep learning startups use it?
1. Automatic speech recognition
Just like we mentioned above, it is one of the most known features of deep learning and big brands use it heavily, for example, Microsoft Cortana, Xbox, Skype Translator, Amazon Alexa, Google Now, Apple Siri, Baidu and iFlyTek voice search, etc. are based on deep learning.
2. Image recognition
As people prefer visual stuff, image recognition gained traction. It is used to analyse documents, pictures connected to a large database and make sure that the fraud is avoided.
3. Natural language processing
Natural language processing is another trendy topic and I even wrote an article about it. You may find it here. It is used by different companies in many industries, most used cases are negative sampling, word embedding, constituency parsing, sentiment analysis, information retrieval, spoken language understanding, machine translation, contextual entity linking, writing style recognition, etc.
4. Drug discovery and toxicology
There are deep learning neural networks for structure-based rational drug design. Researchers enhanced deep learning for drug discovery by combining data from a variety of sources. Now, deep learning is used to predict novel candidate biomolecules for several disease targets, most notably treatments for the Ebola virus, etc.
5. Customer relationship management
It is used a lot in direct marketing for CRM automation. It is good to approximate the value of possible direct marketing actions over the customer state lifetime value.
6. Recommendation systems
Recommendation systems have used deep learning to extract meaningful features for recommendations. It has been applied for learning user preferences from multiple domains.
7. Bioinformatics
It is also used to predict gene ontology annotations, gene-function relationships and sleep quality based on data from wearables and predictions of health complications from Electronic health record data.
8. Gesture Recognition
Gesture Recognition is the latest addition in the area of machine learning which deals with recognising the gestures made by the human face. The signals that are emitted from the sensors are able to detect the emotion by energy, time delay, and frequency shift. It is also able to identify the object and its characteristics.
10 Deep Learning Startups
Deep learning startups come up with absolutely amazing ideas and projects. Let’s look at the brightest ones; these examples are just a small sample of the many companies that are using deep learning to do innovative and exciting things.
1. Bay Labs
Bay Labs is the first one on my list of deep learning startups. It is among the startups applying deep learning to medical imaging to help in diagnosis and management of heart disease. They want to push the limits of deep learning to make an impact on healthcare. By improving access, value, and quality to medical imaging, they hope to promote and advance healthcare in both the developed and developing world. At Bay Labs, they believe that deep learning has potential to impact the leading cause of death “ cardiovascular disease dramatically.
2. Canary
Canary is a New York City-based deep learning startup with a mission to make people safer and more connected to their homes. Canary is the world’s first smart home security device for everyone. Canary contains an HD video camera and sensors that track everything from temperature and air quality to vibration, sound, and movement. It is controlled entirely from your smartphone; Canary alerts you when it senses anything out of the ordinary, from sudden temperature spikes that can indicate a fire, to sound and vibration that could mean an intrusion. Over time, Canary learns your home’s rhythms to send even smarter alerts. Watch the video here.
3. Knit Health
Knit Health is a sleep vision company whose mission is to help families sleep better and stay healthier. Combining novel computer vision and deep learning technologies, Knit can provide families with the personalised insights, suggestions and risk factors about what happens at night, all with just a camera. Knit is currently working on replacing the need for a sleep lab, providing a human-centred and clinically accurate platform for sleep management. Knit’s sleep platform can learn and track critical markers of sleep issues from breathing to sleep quality to nighttime behaviours all without wearables or wires. With clinical accuracy, Knit can turn this data into actionable insights for both families and doctors to help in the assessment and treatment of sleep issues.
4. BenchSci
BenchSci is a machine learning platform that helps biomedical researchers find the best biological compounds for their experiments. It was born as a result of the common struggle with browsing millions of scientific publications to find the antibodies best suited for our experiments. BenchSci is a platform that extracts usage evidence from scientific papers and organises it around antibodies. With BenchSci, scientists can find the best antibodies within minutes. Watch the video here.
5. CarePredict
CarePredict is designed to solve a specific challenge in senior care: family, friends and caregivers of a senior may not notice the precursors to declines in health and hence do not intervene in the time leading to hospital admissions of easily preventable issues. For example, a senior entering into a depressive phase will start having restless sleep patterns, loss of hygiene and changes in eating patterns several days before the episode. Carepredict is solving the continuous observation problem for the senior market with the very first wearable designed for seniors that track their activities of daily living, from waking up, bathing, sleeping, quality of sleep, to brushing teeth, eating, drinking, cooking and more. It gives useful insights. Watch the video here.
6. GrokStyle
GrokStyle is a deep learning AI company. GrokStyle is developing software for a visual search to enable instance recognition of an object. Their mission is to bridge the gap between inspiration and retail. They are experts in search and recommendation. The techniques we are developing can be applied broadly to domains like interior design, apparel search, real estate search, product lookup, etc. Given a photo, they answer questions like What is this product? , Where can I buy it? , What goes with this? GrokStyle was recently named one of the 100 most promising private AI companies globally by CB Insights. Watch the video here.
7. Drive.ai
Drive.ai is a Silicon Valley deep learning startup founded by former lab mates out of Stanford University’s Artificial Intelligence Lab. They are creating Deep Learning for Autonomous Vehicles. They started this project because they believe that this technology has the potential to save lives and transform industries. Watch the video here.
8. Enway
They develop the software stack for autonomous service vehicles. They believe in teaming up autonomous vehicles and human labour to make jobs like street sweeping or trash collection safer, easier and more efficient.
9. Visenze
Visenze simplifies search and categorisation in your image database with visual search and image recognition via an API integration. It develops commercial applications that use deep learning networks to power image recognition and tagging.1 Customers can use pictures rather than keywords to search a company’s products for matching or similar items. Media owners and brands use ViSenze to turn images into immediate engagement opportunities such as product recommendations and Ad targeting.
10. Atomwise
And the last one on the list of promising deep learning startups is Atomwise. It applies deep learning networks to the problem of drug discovery. Atomwise uses deep learning networks to help discover new medicines, to explore the possibility of repurposing known and tested drugs for use against new diseases.
3 Deep learning books that are worth reading
Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world regarding a hierarchy of concepts. The book offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularisation, optimisation algorithms, convolutional networks, sequence modelling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Also, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning: A Practitioner’s Approach by Josh Patterson, Adam Gibson
Reading this book, you will dive into machine learning concepts in general, as well as deep learning in particular. You will understand how deep networks evolved from neural network fundamentals and you will explore the major deep network architectures, including convolutional and recurrent. Also, you will learn how to map specific deep networks to the right problem.
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms by Nikhil Buduma, Nicholas Locascio
Deep learning has become an extremely active area of research. In this practical book, authors provide examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. However, deep learning is still a pretty complex and difficult subject to understand. This book will give you a solid foundation of deep learning understanding.
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