In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM), explain how these terms relate to AI

Usually used as a simple example for how deep learning neural networks work, a feed-forward neural network is the most basic, vanilla general kind of neural network. In this episode of the AI Today

In order for machine learning systems to work, they need to be trained on data. But there are different types of data and depending on the situation you may want to use one type of data over another,

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Pre-Trained Model and Transfer Learning, explain how these terms relate to AI and why it's important to

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,

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 kn

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 abo

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

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 thi

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 im