AI Today Podcast

As AI applications become more complex and data-intensive, the need for scalable and efficient storage solutions becomes increasingly important. AI models require vast amounts of data to be processed, and as the size and complexity of these models continue to grow, so does the ne...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms CPU, GPU, TPU, and Federated Learning, explain how these terms relate to AI and why it’s important to know about them.
Want to dive deeper into an understanding of artificial intellige...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Tokenization and Vectorization, explain how these terms relates to AI and why it’s important to know about them.
Want to dive deeper into an understanding of artificial intelligence, m...

In order for machine learning systems to work they need to be trained on data. But as the old saying goes “garbage in is garbage out” so you need to make sure you have a dataset of prepared data that is cleaned so you can incrementally train a machine learning model to perform a ...

Backpropagation was one of the innovations by Geoff Hinton that made deep learning networks a practical reality. But have you ever heard of that term before and know what it is at a high level? In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define ...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Loss Function, Cost Function and Gradient Descent, explain how these terms relates to AI and why it’s important to know about them.
Show Notes:
• FREE Intro to CPMAI mini course
• CPMA...

Deep Learning is powering this current wave of AI interest. But do you really know what Deep Learning is? In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms hidden layer and deep learning, explain how these terms relates to AI and why ...

The Perceptron was the first artificial neuron. The theory of the perceptron was first published in 1943 by McCulloch & Pitts, and then developed in 1958 by Rosenblatt. So yes, this was developed in the early days of AI. In this episode of the AI Today podcast hosts Kathleen Walc...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Bias, Weight, Activation Function, Convergence, and ReLU and explain how they relate to AI and why it’s important to know about them.
Show Notes:
• FREE Intro to CPMAI mini course
• CP...

If we can replicate neurons and how they are connected, can we replicate the behavior of our brains? In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms (Artificial) Neural Networks, Node, and layer, and explain how they relate to AI an...

For a number of reasons, it can be important to reduce the number of variables or identified features in input training data so as to make training machine learning models faster and more accurate. But what are the techniques for doing this? In this episode of the AI Today podcas...

For time-consuming parts of the machine learning workflow people often look for tricks and techniques to help speed up the process. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms feature and feature engineering, explain how they re...