In the last video in this series, we discussed the biologically inspired structure of deep leaning neural networks and built up an abstracted model based on that. We then went through the basics of how this model is able to form representations from input data. The focus of this video then will continue right where the last one left off, as we delve deeper into the structure and mathematics of neural nets to see how they form their pattern recognition capabilities!
This video is the sixth in a multi-part series discussing computing. In this video, we’ll be discussing the rise of GPU computing and the role it will play in AI computational tasks.
Deep learning is a sub-field of AI that has taken the world by storm, in large part, since the start of this decade. In this sixth video in my artificial intelligence series and as for the purpose of this deep learning series, we’ll explore why it has exploded in popularity, how deep learning systems work and their future applications, so sit back, relax and join me on an exploration into the field of deep learning!
The focus of this video is on artificial neural networks, more specifically – their structure.
A visual introduction to the structure of an artificial neural network.
This episode helps you compare deep learning vs. machine learning. You’ll learn how the two concepts compare and how they fit into the broader category of artificial intelligence. During this demo we will also describes how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting.
This video discusses the basics of Artificial Intelligence, Machine Learning and Deep Learning.
Visual illustration of connection between neural network architecture, hyperparameters, and dataset characteristics.
Very simple explanation of a neural network using an analogy that even a high school student can understand it easily. I will discuss using a simple example various concepts such as what is neuron, error backpropogation algorithm, forward pass, backward pass, neural network training etc.