Artificial Intelligence, Machine Learning and Deep Learning

• Machine Learning and Deep Learning
• Now that we better understand what AI actually means we can take a closer look at machine learning and deep learning to draw a clear distinction between these two.
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• Artificial Intelligence: a program that can sense, reason, act,, and adapt.
• Machine Learning: algorithms whose performance improves as they are exposed to more data over time
• Deep Learning: a subset of machine learning in which multilayered neural networks learn from vast amounts of data.
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• What is machine learning?
• Machine learning can lead to a variety of automated tasks. It affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for optimal trades. Machine learning requires complex math and a lot of coding to achieve the desired functions and results. Machine learning also incorporates classical algorithms for various kinds of tasks such as clustering, regression, or classification. We have to train these algorithms on large amounts of data. The more data you provide for your algorithm, the better your model gets

Machine learning is a relatively old field and incorporates methods and algorithms that have been around for dozens of years, some of them since the 1960s. These classic algorithms include the Navies Bayes Classifier and the Support Vector Machines both of which are often used in data classification. In addition to classification, there are also cluster analysis algorithms such as the K-Means and tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tone.

The training component of a machine learning model means the model tries to optimize along a certain dimension. In other words, machine learning models try to minimize the error between their predictions and the actual ground truth values.

Deep Learning

Deep learning is a young subfield of artificial intelligence based on artificial neural networks. Since deep learning algorithms also require data in order to learn and solve problems, we can also call it a subfield of machine learning. The terms machine learning and deep learning are often treated as synonymous. However, these systems have different capabilities. Deep learning uses a multi-layered structure of algorithms called the neural network.

Artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models could never solve.All recent advances in intelligence 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 remain primitive and Netflix would have no idea which movies or TV series to suggest.We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and deep learning. This is the best and closest approach to true machine intelligence we have so far because deep learning has two major advantages over machine learning.

DEEP LEARNING IS BETTER THAN MACHINE LEARNING

The first advantage of deep learning over machine learning is the redundancy of feature extraction. Long before we used deep learning, traditional machine learning methods decision trees, SVM, Naïve Bayes classifier, and logistic regression were most popular. These are otherwise known as flat algorithms. In this context “flat” means these algorithms cannot typically be applied directly to raw data such as .csv, images, text, etc. Instead, we require a preprocessing step called feature extraction.

When it comes to deep learning models, we have artificial neural networks, which don’t require feature extraction. The layers are able to learn an implicit representation of the raw data on their own. A deep learning model produces an abstract, compressed representation of the raw data over several layers of an artificial neural network. We then use a compressed representation of the input data to produce the result. The result can be, for example, the classification of the input data into different classes.

During the training process, the neural network optimizes this step to obtain the best possible abstract representation of the input data. Deep learning models require little to no manual effort to perform and optimize the feature extraction process. In other words, feature extraction is built into the process that takes place within an artificial neural network without human input. If you want to use a machine learning model to determine whether a particular image shows a car or not, we humans first need to identify the unique features of a car's shape, size, windows, and wheels,  extract these features, and give them to the algorithm as input data. The machine learning algorithm would then perform a classification of the image. That is, in machine learning, a programmer must intervene directly in the classification process.

 Another major advantage of deep learning, and a key part in understanding why it’s becoming so popular, is that it’s powered by massive amounts of data. The era of big data technology will provide huge amounts of opportunities for new innovations in deep learning. Deep learning models tend to increase their accuracy with the increasing amount of training data, whereas traditional machine learning models such as SVM and Naïve Bayes classifier stop improving after a saturation point. Deep learning models scale better with a larger amount of data.