If I had to learn deep learning with Python all over again, I would start with Grokking Deep Learning, written by Andrew Trask.

Welcome to this enlightening journey through the complex but fascinating world of Machine Learning, Deep Learning, and Foundation Models.

This guide provides a simple definition for deep learning that helps differentiate it from machine learning and AI along with eight practical examples of how deep learning is used today.

Deep learning pioneers Deep Yoshua Bengio, Geoffrey Hinton, and Yann LeCun outlines future directions for research in ACM paper.

Deep learning refers to the simulation of networks of neurons that gradually “learn” to recognize images, understand speech or even make decisions on their own.

Deep learning comes with no such guarantees. Nevertheless, there are many businesses that are waking up to the idea of building new services powered by deep learning.

Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power.

The idea is to allow any company to deploy a deep-learning model without the need for specialized hardware. It would not only lower the costs of deep learning but also make AI more widely accessible.

The visual data sets of images and videos amassed by the most powerful tech companies have been a competitive advantage, a moat that keeps the advances of machine learning out of reach from many ...