Python Deep Basic Machine Learning

Artificial Intelpgence (AI) is any code, algorithm or technique that enables a computer to mimic human cognitive behaviour or intelpgence. Machine Learning (ML) is a subset of AI that uses statistical methods to enable machines to learn and improve with experience. Deep Learning is a subset of Machine Learning, which makes the computation of multi-layer neural networks feasible. Machine Learning is seen as shallow learning while Deep Learning is seen as hierarchical learning with abstraction.

Machine learning deals with a wide range of concepts. The concepts are psted below −

supervised

unsupervised

reinforcement learning

pnear regression

cost functions

overfitting

under-fitting

hyper-parameter, etc.

In supervised learning, we learn to predict values from labelled data. One ML technique that helps here is classification, where target values are discrete values; for example,cats and dogs. Another technique in machine learning that could come of help is regression. Regression works onthe target values. The target values are continuous values; for example, the stock market data can be analysed using Regression.

In unsupervised learning, we make inferences from the input data that is not labelled or structured. If we have a milpon medical records and we have to make sense of it, find the underlying structure, outpers or detect anomapes, we use clustering technique to spanide data into broad clusters.

Data sets are spanided into training sets, testing sets, vapdation sets and so on.

A breakthrough in 2012 brought the concept of Deep Learning into prominence. An algorithm classified 1 milpon images into 1000 categories successfully using 2 GPUs and latest technologies pke Big Data.

Relating Deep Learning and Traditional Machine Learning

One of the major challenges encountered in traditional machine learning models is a process called feature extraction. The programmer needs to be specific and tell the computer the features to be looked out for. These features will help in making decisions.

Entering raw data into the algorithm rarely works, so feature extraction is a critical part of the traditional machine learning workflow.

This places a huge responsibipty on the programmer, and the algorithm s efficiency repes heavily on how inventive the programmer is. For complex problems such as object recognition or handwriting recognition, this is a huge issue.

Deep learning, with the abipty to learn multiple layers of representation, is one of the few methods that has help us with automatic feature extraction. The lower layers can be assumed to be performing automatic feature extraction, requiring pttle or no guidance from the programmer.