The broad term for when computers learn from data is machine learning. It speaks of the nexus between statistics and computer science, where algorithms are utilized to carry out certain tasks without being explicitly coded; instead, they identify patterns in the data and generate predictions as soon as fresh data is received. Depending on the data used to feed the algorithms, the learning process for these algorithms can either be supervised or unsupervised.
The straightforward linear regression technique is an example of a conventional machine learning algorithm. Take the case where you wish to forecast your salary based on the number of years you spent in higher education. You must define a function in the first step, such as income = y + x * years of schooling. Then, provide a set of training data to your algorithm. This may be a straightforward table providing information on the number of years of higher education certain people have and the salary that goes along with it. After that, let your algorithm create the boundary, maybe using an OLS regression. You may now input some test data into the program, such as your own years of higher education, and see it forecast your salary.
Moving further down the rabbit hole, it is possible to think of deep learning algorithms as an advanced and theoretically challenging progression of machine learning algorithms. The topic has recently attracted a lot of interest, and with good reason: Recent advancements have produced outcomes that were previously unthinkable. Deep learning is the term used to describe algorithms that examine data in a logical manner, much like a person would. Keep in mind that both supervised and unsupervised learning can lead to this. Deep learning applications employ an artificial neural network, which is a layered framework of algorithms. Such architecture was influenced by the biological neural network of the human brain, creating a learning process that is far more powerful than that of a typical computer.
Think about the neural network example from the image above. The input layer is the layer on the left, while the output layer is the one on the right. Because the values of the middle layers cannot be seen in the training set, they are known as hidden layers. Hidden layers are, to put it simply, computed values that the network uses to perform its magic. A network is deeper the more hidden layers there are between the input and output layer. Deep neural networks are often defined with two or more hidden layers. Overall, the deep learning algorithms require very minimal human input due to autonomous feature engineering and self-learning characteristics.
Deep learning is now being employed in many different industries. These algorithms are being used in healthcare to diagnose and treat a range of diseases. For example, medical professionals deploy deep learning methods to grade different types of cancer. The military use these algorithms to recognize items from satellites, for example, to indicate safe or risky areas for its troops. We’re also at a state of usage in the manufacturing space where both predictive maintenance and defect detection are greatly benefitting which allows the production of higher quality goods. What kind of projects can you imagine deploying deep learning algorthims toward in your own life?