Artificial Intelligence- Machine Learning- Deep Learning

Machine learning and deep learning are subfields of AI As a whole, artificial intelligence contains many subfields, including: Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude. A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data. Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition. Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings. Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks. While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly." Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. The type of machine learning from our previous example, called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous datasets fed. Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling. These algorithms do not have output categories or labels on the data (the model trains with unlabeled data). What Is Deep Learning? Some consider deep learning to be the next frontier of machine learning, the cutting edge of the cutting edge. You may already have experienced the results of an in-depth deep learning program without even realizing it! If you’ve ever watched Netflix, you’ve probably seen its recommendations for what to watch. And some streaming-music services choose songs based on what you’ve listened to in the past or songs you’ve given the thumbs-up to or hit the “like” button for. Both of those capabilities are based on deep learning. Google’s voice recognition and image recognition algorithms also use deep learning. Just as machine learning is considered a type of AI, deep learning is often considered to be a type of machine learning—some call it a subset. While machine learning uses simpler concepts like predictive models, deep learning uses artificial neural networks designed to imitate the way humans think and learn. You may remember from high school biology that the primary cellular component and the main computational element of the human brain is the neuron and that each neural connection is like a small computer. The network of neurons in the brain is responsible for processing all kinds of input: visual, sensory, and so on. With deep learning computer systems, as with machine learning, the input is still fed into them, but the info is often in the form of huge data sets because deep learning systems need a large amount of data to understand it and return accurate results. Then the artificial neural networks ask a series of binary true/false questions based on the data, involving highly complex mathematical calculations, and classify that data based on the answers received. So although both machine and deep learning fall under the general classification of artificial intelligence, and both “learn” from data input, there are some key differences between the two. If you’d like to learn more specifically about deep learning, by the way, you can check out this Introduction to Deep Learning tutorial. It’s also worth learning separately about deep learning with TensorFlow, as TensorFlow is one of the most popular libraries for implementing deep learning.