Caltech Library is offering an introductory sequence of workshops on Machine Learning, Deep Learning, and Natural Language Processing. These workshops are designed for researchers in all areas with an interest in machine learning and language processing. 

Part One: Introduction to Machine Learning

The first workshop introduces traditional machine learning techniques using the Python Scikit Learn package.

Part Two: Introduction to Deep Learning

Workshops 2-5 cover deep learning and neural networks using the Python Keras package and TensorFlow. Topics covered will include the model building, training, optimization and performance measures, and the two principal types of neural network: convolutional and recurrent.

Part Three: Natural Language Processing

Workshops 6 and 7 will focus on Natural Language Processing, starting with classic NLP processing methods using NLTK and SpaCy, and closing with the application of neural networks to NLP, particularly the revolutionary Transformer architecture that powers Generative Artificial Intelligence.

About the workshops

The workshops are designed to be “hands on” and require some familiarity with basic Python programming and the Unix Shell, such as that provided through the Library’s introductory workshops. Although concepts and skills are cumulative through the sequence of workshops, the workshops will be as free-standing as possible. The Deep Learning sequence will be the most inter-dependent. Recordings of workshops will be made available to registered participants.

Workshops will be online, on selected Tuesdays and Fridays at Noon. There is one registration for all seven workshops. The Zoom link for all workshops will be included in the registration confirmation.

1. Tuesday, April 9, 2024: An Introduction to Machine Learning with Scikit Learn

   • Topics: Scikit Learn, processing data, algorithms, clustering, dimensionality reduction

2. Tuesday, April 16, 2024: Deep Learning 1: Introduction to Deep Learning and Neural Networks

   • Topics: characteristics, learning, loss, optimization, training, appropriate types of problems

3. Friday, April 26, 2024: Deep Learning 2: Building a Neural Network for Classification

   • Topics: Keras, building the model, training, prediction, classification, performance

4. Friday, May 3, 2024: Deep Learning 3: The Neural Network Training Process

   • Topics: regression, optimization, monitoring, performance, fitting

5. Friday, May 10, 2024: Deep Learning 4: Advanced Neural Network Layer Types

   • Topics: convolutional neural networks (image classification), recurrent neural networks (for sequential data)

6. Friday, May 17, 2024: Introduction to Natural Language Processing

   • Topics: preprocessing text, word context and semantics, parts of speech and entity recognition, sentiment analysis, word vectors

7. Friday, May 24, 2024: NLP with Transformer Models

   • Topics: the evolution of Large Language Models, “Attention” and the Transformer architecture, using the Python Transformers package for NLP tasks