University of Pennsylvania

Deep Learning Essentials

Chris Callison-Burch
Pratik Chaudhari

Instructors: Chris Callison-Burch

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the history and context of the deep learning field, and explore what "intelligence" really means.

  • Explore deep learning models like the perceptron, neural networks and backpropagation, and study the techniques that drive them.

  • Code a project using Python where you will preprocess data and use your data to train a Support Vector Machine (SVM.)

Details to know

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Recently updated!

September 2024

Assessments

12 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the AI and Machine Learning Essentials with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

In this module, we'll first peek through history, talk about the different ways in which people have attempted to build artificial intelligences in the past and explore what intelligence is made up of. Then, we'll start our investigation into an early model called the perceptron.

What's included

11 videos2 readings3 assignments1 discussion prompt

This module, we will continue exploring the perceptron. We'll delve into stochastic gradient descent (SGD), a fundamental optimization technique that enables the perceptron, and other models, to learn from data by iteratively updating the model's parameters to minimize errors. Afterward, we will look at kernel methods. These techniques can separate two sets of points in more complicated ways, drawing inspiration from how the human eye works.

What's included

11 videos3 assignments1 programming assignment

This module, we will move to exploring fully-connected networks. These networks are sophisticated models that can be thought of as a perceptron sitting on top of another perceptron, continuing in such a fashion. Each layer in a fully-connected network takes inputs from the layer below it, working to separate data points (such as the red and the blue scattered points) a little better than the one before it, and then passes it on to the next layer.

What's included

8 videos3 assignments1 discussion prompt

We will finish this course by looking at backpropagation, which is an algorithm to train neural networks to find the best set of weights that minimize error on the data. Backpropagation applies the chain rule from calculus to efficiently calculate gradients of the loss function with respect to the weights, enabling the model to update its weights in the opposite direction of the gradient. We'll discuss the importance of typical datasets consisting of images, sentences, and sounds, and how neural networks can learn from the spatial regularities present in such data.

What's included

8 videos1 reading3 assignments1 programming assignment

Instructors

Chris Callison-Burch
7 Courses2,069 learners

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Recommended if you're interested in Machine Learning

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