What exactly is deep learning? Why has it become so popular? And how are companies using deep learning? Get the answers to all these questions and more in this deep dive!
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.
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.
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.
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.
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