Python Deep Learning: Parts 1-2

March 28, 2022, 9:00am to March 30, 2022, 12:00pm

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If you are from the UCB campus there's no more waitlist! But after registering above, please do fill out the affiliations form if you have not done so at least once before:

Location: Remote via Zoom. Link will be sent on the morning of the event.

Recordings: This D-Lab workshop will be recorded and made available to UC Berkeley participants for a limited time. Your registration for the event indicates your consent to having any images, comments and chat messages included as part of the video recording materials that are made available.

Date & Time:This workshop is a 2-part series running from 9am-12pm:

  • Part 1: Monday, March 28
  • Part 2: Wednesday, March 30

Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.


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 Introduction to Machine Learning: Parts 1-2 or equivalent knowledge.

This is an advanced level workshop. Participants should be intermediate Python users and have had some prior exposure to machine learning.

We assume the following background:

  • D-Lab's Python Machine Learning Fundamentals (6 hours)
  • Or, comparable experience/training, assuming familiarity with:
    • Basic Python syntax
    • Train/validation/test splitting
    • Dataset cleaning
    • Overfitting / underfitting / generalization
    • Hyperparameter customization
    • Basic linear algebra (vector, matrix, etc.)
    • Basic statistics (linear regression)

Workshop materials:

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