Formerly: Deep Learning and Artificial Intelligence with TensorFlow and Keras
Deep learning, a branch of artificial intelligence and machine learning, uses multilayered neural networks to create highly accurate prediction models for image recognition, object detection, language translation, speech recognition, and other tasks. In this course, students will use open source and industry-standard machine learning libraries to build and deploy deep learning models.
Students will build deep learning prediction models of different complexities, from simple linear logistic regression to major categories of neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs).
By the end of the course, students will be proficient in best practices of using standard machine learning frameworks such as Pytorch, TensorFlow and Keras, and using datasets for solving common machine learning problems.
The class prepares students to pursue a career in data sciences and AI model development.
At the conclusion of the course, you should be able to
- Use common deep learning architectures such as CNN and RNN
- Discuss the significance of hyperparameters in the architectures
- Prepare data for deep learning using Pandas and NumPy, the de facto standard for data prep in Python
- Write scalable code and develop machine learning models that can be used to train deep learning architectures on real-world business problems
- Debug and understand the inner working of deep learning architectures
- Deep learning with standard machine learning frameworks including TensorFlow, Keras and Pytorch
- Multilayer perceptrons
- Advanced multilayer perceptrons
- Convolutional neural networks
- Image processing CNN architectures
- Recurrent neural networks
- RNN - prediction with multilayer perceptron
- RNN - prediction with long short term memory networks
Note(s): Students are required to bring laptops for the classroom and work with Python3/ Jupyter Notebook environment.
Skills Needed: Moderate level of computer programming ability in Python, comfortable with an editor, familiarity with command-line operations on a laptop, and a basic understanding of Machine Learning models.
- Save Your Seat
Help us confirm course scheduling. Enroll at least seven days before your course starts.
- Accessing Canvas
Learn more about gaining access to your course on Canvas in our FAQ section.
Accessibility and Accommodation
For accessibility questions or to request an accommodation, please visit Access for Students with Disabilities or email the Extension registrar.
Finance Your Education
Here are ways to pay for your education.
Sections Open for Enrollment:
|Date:||Start Time:||End Time:||Meeting Type:||Location:|
|Sat, 01-20-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 01-27-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 02-03-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 02-10-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 02-17-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 02-24-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 03-02-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 03-09-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 03-16-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|
|Sat, 03-23-2024||9:00 a.m.||12:00 p.m.||Flexible||SANTA CLARA / REMOTE|