AI Today Podcast

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Model Validation, Validation Data, Test Data, Cross-Validation, explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Model Tuning and Hyperparameter, explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer discuss and define at a high level the terms OpenAI, GPT, DALL-E, and Stable Diffusion, and share why you should know these terms and how they relate to the overall AI landscape.
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Large Language Models (LLMs) are all the rage these days. It’s hard to have a conversation about AI without ChatGPT, Google Bard, or LLMs being brought up. However, what if these LLMs could learn more about you individually and provide a much more hyperpersonalized experience and...

Transformer models have proven to be especially powerful for Natural Language Processing applications and image generation, and have been popularized by models such as GPT-3, Stable diffusion, and BERT. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelze...

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Boltzmann Machine, explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Decoder, AutoEncoder, Generative Adversarial Network (GAN), explain how these terms relate to AI and why it’s important to know about them.
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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Convolutional Neural Network (CNN), explain how these terms relate to AI and why it’s important to know about them.
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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 and why it’s important to know about them.
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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 podcast hosts Kathleen Walch and Ron Schmelzer define the term Feed-Forward Neur...

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, or a combination of different types. In this episode of the AI Today podcast hos...

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 know about them.
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