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The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.
We start with a review of what deep learning is and then unpack what neural networks are and how they work. We then jump straight into Python, using the Keras library to build neural networks. We will explore how different architectures affect performance of predicting handwritten digit images.
Lastly, we explore a specific flavor of neural networks, the convolutional neural network. We review how it’s different from a standard vanilla neural network, and build different architectures to test how well they perform on the classification of animal and vehicle image classification.
Prerequisites: D-Lab’s Python Machine Learning Fundamentals (6 hours) series or equivalent introductory machine learning knowledge.
Registration: https://dlab.berkeley.edu/cas?destination=/events/python-deep-learning-parts-1-2/2023-04-11
Workshop Materials: https://github.com/dlab-berkeley/Python-Deep-Learning