For better or worse, it seems as though the term “artificial intelligence” (AI) is becoming synonymous with modern machine learning. Whereas AI used to encompass many types of computational techniques used in the simulation of intelligence in machines, it almost exclusively refers to the state of the art (SOTA) advances in one branch of modern machine learning in particular: deep learning.
Deep neural networks had already been around for some time when Krizhevsky, Sutskever and Hinton’s ImageNet victory in October 2012 kicked off the modern deep learning revolution. Since then, deep learning has gone on to conquer SOTA results in almost every AI subfield and domain it has been put up against: computer vision; natural language processing; speech recognition; medical image analysis; image reconstruction; text generation; and much more.
Simply put, if you want to be involved in modern AI development, you need to understand deep learning. This is where Simplilearn’s Artificial Intelligence And Deep Learning Full Course comes in.
Still image from Simplilearn’s Artificial Intelligence And Deep Learning Full Course
Here is how the course YouTube page defines both artificial intelligence and deep learning, and their connection:
What is Artificial Intelligence?
Artificial Intelligence is the process of building intelligent machines from vast volumes of data. Systems learn from past learning and experiences and perform human-like tasks. It enhances the speed, precision, and effectiveness of human efforts. AI uses complex algorithms and methods to build machines that can make decisions on their own. Machine Learning and Deep learning forms the core of Artificial Intelligence.
What is Deep Learning?
Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with artificial neural networks, which are designed to imitate how humans think and learn.
Simplilearn has constructed this video course in order to help viewers understand the basics of AI and deep learning, and how to use AI and DL algorithms in their own models.
Directly from its site, here is what you can expect the course to cover:
- Basics of AI
- Future of AI
- AI of Detail
- What is Deep Learning
- Neural Networks
- Tensorflow Object Detection
- Recurrent Neural Network
- What are GANs?
- Keras Tutorial
- Deep Learning Interview Questions
You can view the 10.5 hour course below or on YouTube directly.
Don’t wait to expand your understanding of artificial intelligence and its most potent modern enabler, deep learning. This thorough course will certainly get you on the right path to doing so.
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master’s degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.