Empower your Team with the Deep Learning Workshop


Deep Learning Workshop Overview

This one-day workshop introduces participants to the foundations of Deep Learning. Participants practice constructing neural networks of various levels of complexity  (e.g., with text, images, etc.) to connect the core ideas to their realization in practical applications.

Keep reading below for more information about this training course or click the button here to fill out a workshop request


We assume participants are comfortable using the Python language and, in particular, working with standard Python tools for data analysis (notably NumPy, Pandas, and Jupyter). No prior experience using libraries for machine/deep learning (e.g., Scikit-Learn, PyTorch, TensorFlow, etc.) is expected. Some prior exposure to calculus and linear algebra is helpful but not mandatory.

Learning Objectives

At the end of this workshop, participants should be able to:


  • Explain the relevance of deep learning in application contexts

  • Identify & explain the role of various components of neural network architectures (e.g., layers, neurons/units, activation functions, weights/biases)


  • Implement simple illustrative examples of forward propagation & back-propagation in Python


  • Define descriptive examples of neural networks with various choices of activation functions, loss functions, optimizers, & network architectures


  • Construct working examples of feed-forward and convolutional neural networks of varying depth & complexity using standard Python frameworks (e.g., PyTorch, TensorFlow)

To be contacted by a Quansight representative, or for more information, please fill out the form at the link below.

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