Have you ever wondered how Google can translate entire paragraphs from one language into another in a matter of milliseconds; how Netflix and YouTube can provide goodrecommendations; howself-driving carsare even possible?
All of these innovations are the product of deep learning and artificial neural networks.
Deep learning is a subset of machine learning, which is a subset of artificial intelligence. Artificial intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine learning represents a set of algorithms trained on data that make all of this possible. Deep learning is just a type of machine learning, inspired by the structure of the human brain.
Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.
The design of the neural network is based on the structure of the human brain. Just as we use our brains to identify patterns and classify different types of information, we can teach neural networks to perform the same tasks on data.
The individual layers of neural networks can also be thought of as a sort of filter that works from gross to subtle, which increases the likelihood of detecting and outputting a correct result. The human brain works similarly. Whenever we receive new information, the brain tries to compare it with known objects. The same concept is also used by deep neural networks.
Neural networks enable us to perform many tasks, such asclustering,classificationorregression.
With neural networks, we can group or sort unlabeled data according to similarities among samples in the data. Or, in the case of classification, we can train the network on a labeled data set in order to classify the samples in the data set into different categories.
In general, neural networks can perform the same tasks asclassical machine learning algorithms (but classical algorithms cannot perform the same tasks as neural networks). In other words, artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models can never solve.
All recent advances in artificial intelligence in recent years are due to deep learning. Without deep learning, we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate would continue to be as primitive as it was 10 years ago before Google switched to neural networks and Netflix would have no idea which movies to suggest. Neural networks are behind all these technologies.
A new industrial revolution is taking place, driven by artificial neural networks and deep learning. At the end of the day, deep learning is the best and most obvious approach to real machine intelligence we’ve ever had.
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Deep learning models are more powerful than machine learning models but why?
The first advantage of deep learning over machine learning is the redundancy of the so-called feature extraction.
Long before we began using deep learning, we relied on traditional machine learning methods includingdecision trees, SVM, naïve Bayes classifier andlogistic regression. These algorithms are also called flat algorithms. “Flat” here refers to the fact these algorithms cannot normally be applied directly to the raw data (such as .csv, images, text, etc.). We need a preprocessing step called feature extraction.
The result of feature extraction is a representation of the given raw data that these classic machine learning algorithms can use to perform a task. For example, we can now classify the data into several categories or classes. Feature extraction is usually quite complex and requires detailed knowledge of the problem domain.
