In this article, I explain about static vs dynamic computational graphs and how to construct them in PyTorch and TensorFlow. This is the magic that allows these frameworks to calculate gradients for your neural networks.
I had explained about the back-propagation algorithm in Deep Learning context in my earlier article. This is a continuation of that, I recommend you read that article to ensure that you get the maximum benefit from this one.
I’ll cover computational graphs in PyTorch and TensorFlow. This is the magic that allows these frameworks to calculate gradients for your neural networks. I’ll start with some introduction to types of computational graphs followed by framework specific details.
All the deep learning frameworks rely on the creation of computation graphs for the calculation of gradient values required for the gradient descent optimization. Generally, you have to build the forward propagation graph and the framework takes care of the backward differentiation for you.
But before starting with computational graphs in PyTorch, I want to discuss static and dynamic computational graphs.
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning.
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.