The most recent breakthrough in deep learning research comes from OpenAI with two astonishing transformers -- GPT-3 and DALL-E [6], the former being an AI novelist and poet and the latter an AI designer and artist. With GPT-3 you can start a novel or a poem with a few sentences or paragraphs and ask the model to complete it. DALL-E model transforms text into many images.

With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool! Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges.

Here's a great introduction to neural networks that explains the core concepts without getting too deep into the mathematical weeds.

With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool! Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges.

With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest?

Autoencoders are a family of neural nets that are well suited for unsupervised learning, a method for detecting inherent patterns in a data set. These nets can also be used to label the resulting patterns. Essentially, autoencoders reconstruct a data set and, in the process, figure out its inherent structure and extract its important features. An RBM is a type of autoencoder that we have previously discussed, but there are several others. Autoencoders are typically shallow nets, the most common of which have one input layer, one hidden layer, and one output layer.

An RBM can extract features and reconstruct input data, but it still lacks the ability to combat the vanishing gradient. However, through a clever combination of several stacked RBMs and a classifier, you can form a neural net that can solve the problem. This net is known as a Deep Belief Network. The Deep Belief Network, or DBN, was also conceived by Geoff Hinton. These powerful nets are believed to be used by Google for their work on the image recognition problem.

Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any...

Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any...

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