Deep learning is a type of machine learning and artificial intelligence (AI) that mimics the way humans obtain certain types of knowledge. It is an important element of data science, including statistics and predictive models. Machine learning and deep learning are two types of AI, with deep learning being a subset of machine learning, which is a subset of AI. Deep learning uses artificial neural networks to mimic the learning process of the human brain, helping machine learning to adapt automatically with minimal human interference.
Many companies claim to incorporate some type of AI into their applications or services, but what does that mean in practice? AI describes when a machine mimics the cognitive functions that humans associate with other human minds, such as learning and problem solving. At an even more basic level, AI can simply be a programmed rule that tells the machine to behave in a specific way in certain situations. Machine learning is a series of algorithms that analyze data, learn from it and make informed decisions based on that knowledge learned. It affects virtually every industry, from computer security malware to weather forecasting and stockbrokers looking for optimal operations.
Machine learning requires complex mathematics and a lot of coding to achieve the desired functions and results. It also incorporates classic algorithms for various types of tasks, such as clustering, regression or classification. To reduce the dimensionality of data and learn more about its nature, machine learning uses methods such as principal component analysis and TsNE. Deep learning is a young subfield of AI based on artificial neural networks.
It also requires data to learn and solve problems, making it a subfield of machine learning. Deep learning has two main advantages over machine learning: redundancy of function extraction and increased accuracy with more data. In traditional machine learning methods (decision trees, SVM, Naïve Bayes classifier and logistic regression), function extraction is necessary to provide an abstract representation of raw data that classic machine learning algorithms can use to perform a task. This step must be adapted, tested and refined in several iterations for optimal results.
Deep learning models don't need function extraction; they can be applied directly to raw data such as images or text. Deep learning models also tend to increase in accuracy as the amount of training data increases, while traditional machine learning models stop improving after a saturation point. This increase in data creation is one of the reasons why deep learning capabilities have grown in recent years. Machine learning engineers are in high demand because neither data scientists nor software engineers have precisely the skills needed for the field of machine learning.
To paraphrase Andrew Ng, chief scientist at China's leading search engine Baidu, co-founder of Coursera and one of the leaders of the Google Brain Project, if a deep learning model can be trained well enough, it can outperform any other AI technique.