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    <item>
      <title>1: Introduction to Neural Networks and Deep Learning; Training Deep NNs</title>
      <link>/resources/15773-sp24-lecture-1-version-3_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This video provides an introduction and overview of the course covering the history and background of the field.&lt;/p&gt;</description>
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      <title>10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning</title>
      <link>/resources/15773-sp24-lecture-10-version-4_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This lecture addresses generative pretrained transformers (GPTs), version differences and nuances of training data, instruction tuning, and adapting base language learning models (LLMs).&lt;/p&gt;</description>
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      <title>11: Generative AI – Text-to-Image Models</title>
      <link>/resources/15773-sp24-lecture-11-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This lecture discusses various text-conditional diffusion models using a transformer architecture, including text-to-image and text-to-video examples.&lt;/p&gt;</description>
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      <title>2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data</title>
      <link>/resources/15773-sp24-lecture-2-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This session introduces various aspects of designing and training deep neural networks using the example of a model for heart disease prediction.&lt;/p&gt;</description>
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      <title>3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch</title>
      <link>/resources/15773-sp24-lecture-3-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;A recap of training flow is given,  then the rest of the session walks through the steps of building a deep neural network in Colab.&lt;/p&gt;</description>
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      <title>4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace</title>
      <link>/resources/15773-sp24-lecture-4-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This lecture covers transfer learning, convolutional neural network (CNN) models, pooling layers, and application examples, including a handbags-shoes classifier.&lt;/p&gt;</description>
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    <item>
      <title>5: Deep Learning for Natural Language – The Basics</title>
      <link>/resources/15773-sp24-lecture-5-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This video provides an introduction to natural language processing, including vectorization, the bag-of-words model, and a demonstration in Colab.&lt;/p&gt;</description>
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      <title>6: Deep Learning for Natural Language – Embeddings</title>
      <link>/resources/15773-sp24-lecture-6-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This lecture continues discussion of natural language processing with a focus on embeddings, including stand-alone and contextual embeddings.&lt;/p&gt;</description>
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      <title>7: Deep Learning for Natural Language – Transformers</title>
      <link>/resources/15773-sp24-lecture-7-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;Transformers are described via an airline travel-related example.&lt;/p&gt;</description>
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      <title>8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning</title>
      <link>/resources/15773-sp24-lecture-8-version-2_mp4/data.json</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This video takes a deeper dive into transformers and how to use them.&lt;/p&gt;</description>
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      <title>9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)</title>
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      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This video introduces next-word prediction using the transformer encoder architecture from the previous class.&lt;/p&gt;</description>
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