Chapter 0 – PyTorch Fundamentals 0:01:45 0. Welcome and "what is deep learning?"

All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. Just to show you the kind of attention they are getting, here is the Google trend for these keywords: If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them.

Machine Learning as the name signifies allows machines to learn with huge volumes of data that an algorithm can process to make predictions. Essentially, machine learning eliminates the need to continuously code or analyze data themselves to solve a solution or present a logic. In other words, this form of AI enables a computer's ability to learn and teach itself to evolve as it is fed new data. And since machine learning deploys an iterative approach to glean from data, this learning process is automated and the models are run until a robust pattern is found. ML software constitutes of two main elements -- statistical analysis and predictive analysis which is used to spot patterns and uncover hidden insights from previous computations without being programmed.

A popular notion about machine learning models is the interpretability -- statistical models like logistic regression yield interpretable models. Historically, financial sector which relies heavily on interpretability uses machine learning models because of its ability to offer an audit trail. On the other hand, neural networks are dubbed as the black boxes since there is no real understanding of how the output was achieved. In other words, one may not be able to ascertain how the exact model works inside and out, but know the learning algorithm that created it. However, according to Zachary Chase Lipton, a Ph.D student at UCSD, machine learning algorithms, for example decision trees, often championed for their interpretability, can also be similarly opaque.

All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them. Let us start with the basics – What is Machine Learning and What is Deep Learning.

All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them. Let us start with the basics – What is Machine Learning and What is Deep Learning.