Over the past few years, deep learning has become another trendy word. It is mostly used 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 that are revolutionizing 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. Basically, 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 a number of 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. The next level takes this simple information, combines it to create 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 an example of a picture 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. 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 analyze a huge amount of information and save costs.
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 algorithms. 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 Do Companies Use Deep Learning?
- Automatic speech recognition. Just like we mentioned above, this is one of the most known features of deep learning and big brands use it heavily. For example, Microsoft Cortana, Skype Translator, Amazon Alexa, Google, and Apple Siri,are based on deep learning.
- Image recognition. As people prefer visual stuff, image recognition has gained traction. It is used to analyze documents and pictures connected to a large database, and to make sure that fraud is avoided.
- Natural language processing. Natural language processing is another trendy topic and I even wrote an article about it. It is used by different companies in many industries, especially for negative sampling, word embedding, sentiment analysis, spoken language understanding, machine translation, contextual entity linking, and writing style recognition.
- 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.
- Customer relationship management. Deep learning 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.
- Recommendation systems. Recommendation systems use deep learning to extract meaningful features for recommendations. It has been applied for learning user preferences from multiple domains.
- 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.
- Gesture recognition. Gesture recognition is the latest addition in the area of machine learning that deals with recognizing the gestures made by the human face. The signals emitted from sensors are able to detect the emotion based on energy, time delay, and frequency shifts. 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.
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 the 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 of 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 dramatically impact the leading cause of death, cardiovascular disease.
Canary is a New York City-based deep learning startup with a mission to make people safer in 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 avideo about Canaryhere.
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 personalized insights, suggestions, and risk factors about what happens when they sleep at night, all with just a camera. Knit is currently working on replacing the need for a sleep lab, providing a human-centered and clinically accurate platform for sleep management. Knit’s sleep platform has the ability to learn and track critical markers of sleep issues from breathing to sleep quality to nighttime behaviors, 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.
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 in order to find the antibodies best suited for our experiments. BenchSci is a platform that extracts usage evidence from scientific papers and organizes it around antibodies. With BenchSci, scientists can find the best antibodies within minutes. Watch a video about BenchScihere.
CarePredict is designed to solve a specific challenge in senior care: family, friends, and caregivers of an elderly person may not notice the precursors to declines in health and hence do not intervene in 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 solves the continuous observation problem for the senior market with the very first wearable designed for seniors that tracks their activities of daily living — from waking up, bathing, sleeping, and quality of sleep to brushing teeth, eating, drinking, cooking, and more. It gives useful insights. Watch avideoabout CarePredicthere.
GrokStyle is a deep learning AI company. GrokStyle is developing software for visual searches 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 they 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?” and “Where can I buy it?” and “What goes with this?” GrokStyle was recently named one of the top 100 most promising private AI companies globally by CB Insights. Watch a video about GrokStylehere.
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 avideo about ithere.
Enway develops the software stack for autonomous service vehicles. They believe in teaming up autonomous vehicles and human labor to make jobs like street sweeping or trash collection safer, easier, and more efficient.
ViSenze simplifies search and categorization 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. 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.
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.
Deep Learning Books That Are Worth Reading
Lastly, let's look over a few deep learning books you might want to read.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. This book offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory, information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, 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 and 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 and 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.
What is the best use case for deep learning? ›
- Image Recognition. Deep learning is beneficial for computer vision applications, as discussed previously. ...
- Speech Processing. ...
- Translation. ...
- Recommendation Engines. ...
- Text Mining. ...
- Analytics. ...
- Forecasting. ...
- Predicting customer service issues.
- Identifying new prospects.
- Personalizing marketing based on customer preferences.
- Automating emails so they're sent when they're most likely to be read.
Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.Where is deep learning mostly used today? ›
Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.What is the biggest advantage of deep learning in AI? ›
One of the biggest advantages of using deep learning approach is its ability to execute feature engineering by itself. In this approach, an algorithm scans the data to identify features which correlate and then combine them to promote faster learning without being told to do so explicitly.What company is leading in deep learning? ›
Google Cloud Vertex AI
Consequently, it's no surprise that Google Cloud stands as one of the world's top machine learning companies. The company's Vertex AI platform unifies its AI, auto ML, deep learning container, and TensorFlow services in one comprehensive package.
Still, IBM remains a market leader in other AI technology, and its AutoML and AutoAI products can help data scientists build and train AI and machine learning models.Which is the most powerful AI company? ›
IBM Cloud. IBM has been a leader in the field of artificial intelligence since the 1950s. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services.Why are AI startups important? ›
Artificial intelligence (AI)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms.
How can AI startups create competitive advantage? ›
Analytics and Feedback for Continuous Improvement
And the only way to properly analyze such enormous data sets is through artificial intelligence. AI can see trends in the data and predict what the customers want, giving you an advantage over the competition.
- Automate Newsletters. ...
- Create Content. ...
- Predict Ad Performance. ...
- Improve Customer Service. ...
- Follow Up with Leads. ...
- Make Accounting Easier. ...
- Get More Insights from Your Data. ...
- AI for Accounting.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.What is the difference between deep learning and AI? ›
Artificial Intelligence is the concept of creating smart intelligent machines. Machine Learning is a subset of artificial intelligence that helps you build AI-driven applications. Deep Learning is a subset of machine learning that uses vast volumes of data and complex algorithms to train a model.Is deep learning AI or ML? ›
Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adapt with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks to mimic the learning process of the human brain.Why is deep learning so powerful? ›
along with large enough data, a deep learning network can learn any mapping from one vector space to another vector space. That's what makes deep learning such a powerful tool for any machine learning task. Abhishek Gupta is the principal data scientist at Talentica Software.What can you create with deep learning? ›
- Self Driving Cars.
- News Aggregation and Fraud News Detection.
- Natural Language Processing.
- Virtual Assistants.
- Visual Recognition.
- Fraud Detection.
Machine Learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images, and text.Why deep learning is the future? ›
Deep learning works like the human brain
It's imitating the way humans acquire certain types of knowledge. Because deep learning processes information in a similar manner, it can be used to do things people can do – for example, learning how to drive a car or identifying a dog in a picture.
Prediction 1: Deep learning networks will demystify computer memory. Prediction 2: Neural architecture search will play a key role in building datasets for DL models.
What problems does deep learning solve? ›
Companies like Microsoft and Google use deep learning to solve difficult problems in areas such as speech recognition, image recognition, 3-D object recognition, and natural language processing. However, deep learning requires considerable computing power to construct a useful model.What is the rocket fuel of AI? ›
Graphics technology is rocket fuel for AI
Graphics rendering computing is finding wide-ranging uses outside of video games, AI being one of them.
Analyst Sanjit Singh of Morgan Stanley has given 10 ratings on AI stock to date, with all Sell recommendations. His calls have a success rate of 100%, generating an average return per call of a whopping 52.09%.
Lucid.AI is the world's largest and most complete general knowledge base and common-sense reasoning engine.Which country is most advanced in AI? ›
Countries leading the way in AI.
|United States of America||4||8.804|
- Software #1: Viso Suite Platform.
- Software #2: Content DNA Platform.
- Software #3: Jupyter Notebooks.
- Software #4: Google Cloud AI Platform.
- Software #5: Azure Machine Learning Studio.
- Software #6: Infosys Nia.
- Software #7: Salesforce Einstein.
- Software #8: Chorus.ai.
- Amazon. Headquarters and locations: With its headquarters in Seattle, Washington, U.S, the company has offices in a number of U.S. cities and around the world. ...
- C3.ai. ...
- DeepMind. ...
- H2O.ai. ...
- IBM. ...
- Meta Platforms. ...
- NICE. ...
For AI companies in marketing, advertising or e-commerce, for example, they could earn a percentage of each completed sale that originated on their AI chatbot. A subset of this model is the “cost-savings share” whereby an AI company is compensated when a customer saves money due to its AI platform.Who are the key players in AI? ›
AI is proven to be a significant revolutionary element of the upcoming digital era. Tech giants like Amazon.com, Inc.; Google LLC; Apple Inc.; Facebook, International Business Machines Corporation, and Microsoft are investing significantly in the research and development of AI.What are the 4 competitive strategies? ›
- Cost leadership strategy. It suits large businesses that can produce a big volume of products at a low cost, and that is why Walmart implemented this strategy. ...
- Differentiation leadership strategy. ...
- Cost focus strategy. ...
- Differentiation focus strategy.
How can AI help business survive and compete? ›
Thanks to AI, companies can use automated machines as indicators to analyze a complete perspective of the objectives of the company, allowing them to make the best decisions for the company.How AI is useful in business? ›
By deploying the right AI technology, your business may gain the ability to: save time and money by automating and optimising routine processes and tasks. increase productivity and operational efficiencies. make faster business decisions based on outputs from cognitive technologies.How do I add an AI to my business? ›
- Step 1: Understand the difference between AI and ML. ...
- Step 2: Define your business needs. ...
- Step 3: Prioritize the main driver(s) of value. ...
- Step 4: Evaluate your internal capabilities. ...
- Step 5: Consider consulting a domain specialist. ...
- Step 6: Prepare your data.
AI/ML has the potential to transform all aspects of a business by helping them achieve measurable outcomes including: Increasing customer satisfaction. Offering differentiated digital services. Optimizing existing business services.How many small businesses use AI? ›
31% of small businesses have adopted AI tools due to the challenges they face with limited staff, time and budgets that leave marketing teams at a disadvantage. 97% of AI adopters say they're experiencing significant time savings in performing marketing tasks as a result of their adoption of AI tools.Why it is called deep learning? ›
Deep Learning is called Deep because of the number of additional “Layers” we add to learn from the data. If you do not know it already, when a deep learning model is learning, it is simply updating the weights through an optimization function. A Layer is an intermediate row of so-called “Neurons”.How is deep learning used in real world? ›
Whether it's Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them. In a similar way, deep learning algorithms can automatically translate between languages.What should I learn first AI or ML? ›
So, should I learn machine learning or artificial intelligence first? If you're looking to get into fields such as natural language processing, computer vision or AI-related robotics then it would be best for you to learn AI first.How does deep learning AI work? ›
How does deep learning work? Deep learning networks learn by discovering intricate structures in the data they experience. By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.Who are the fathers of AI? ›
John McCarthy was one of the most influential people in the field. He is known as the "father of artificial intelligence" because of his fantastic work in Computer Science and AI. McCarthy coined the term "artificial intelligence" in the 1950s.
Is deep learning a subset of AI? ›
Deep learning is a subset of machine learning, and machine learning is a subset of Artificial Intelligence, an important parameter for any intelligent computer programme. In other terms, all machine learning is Artificial Intelligence, but not all Artificial Intelligence is machine learning, and so on.Can you have AI without machine learning? ›
Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. For example, symbolic logic – rules engines, expert systems and knowledge graphs – could all be described as AI, and none of them are machine learning.What deep learning is used for by giving use case examples? ›
Deep Learning use cases have been widely used for knowledge discovery and Predictive Analytics. For example, Google uses DL to build powerful voice- and image-recognition algorithms. Netflix and Amazon use DL in their recommendation engines, and MIT researchers use DL for Predictive Analytics.What are the real world applications of deep learning? ›
- Fraud detection.
- Customer relationship management systems.
- Computer vision.
- Vocal AI.
- Natural language processing.
- Data refining.
- Autonomous vehicles.
Deep learning works like the human brain
Deep learning is also used to automate predictive analytics – for example, identifying trends and customer buying patterns so a company can gain more customers and keep more of them.
- Interpretability and explainability are paramount.
- Smaller amounts of relatively simple data.
- Straightforward feature engineering.
- Limited computational power.
- Limited time, need for faster prototyping and operationalization.
- Need for varied algorithm choices.
- Accuracy of test dataset results is acceptable.
Common Applications Of Deep Learning
It is used in various end use industries, from medical devices to automated driving, and more. Automotive researchers use deep learning to detect objects automatically, such as stop traffic lights and signs.
Machine Learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images, and text.What is an example of deep learning in AI? ›
Deep learning utilizes both structured and unstructured data for training. Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more.What are limitations of deep learning? ›
What Are The Limitations Of Deep Learning? Deep learning works only with large amounts of data. Training it with large and complex data models can be expensive. It also needs extensive hardware to do complex mathematical calculations.
What are deep learning models? ›
Deep learning models are widely used in extracting high-level abstract features, providing improved performance over the traditional models, increasing interpretability and also for understanding and processing biological data.What is the future scope of deep learning? ›
Prediction 1: Deep learning networks will demystify computer memory. Prediction 2: Neural architecture search will play a key role in building datasets for DL models.What is AI most commonly used for? ›
- Personalized Shopping. ...
- AI-powered Assistants. ...
- Fraud Prevention. ...
- Administrative Tasks Automated to Aid Educators. ...
- Creating Smart Content. ...
- Voice Assistants. ...
- Personalized Learning. ...
- Autonomous Vehicles.
Netflix uses machine learning algorithms to predict the viewer patterns and understand when there will be general increases and decreases in viewers of spikes in viewing a certain movie or show.Which deep learning framework is fastest? ›
Keras. Francois Chollet originally developed Keras, with 350,000+ users and 700+ open-source contributors, making it one of the fastest-growing deep learning framework packages. Keras supports high-level neural network API, written in Python.When should you avoid deep learning? ›
Oftentimes, you can be working as a data scientist at a smaller company, or perhaps at a startup. In these cases, you would not have much data and you might not have a big budget. You would, therefore, try to avoid the use of deep learning algorithms.When should you not use deep learning? ›
Don't use machine learning without labeled data and in-house expertise. Most deep learning models require labeled data and an expert team to train the models and put them in production. It is advisable not to use deep learning algorithms to deliver projects if you don't have enough labeled data and a dedicated team.