Introduction to Deep Learning for Computer Vision

Understanding How Deep Learning Powers Visual Intelligence Deep learning has transformed computer vision by enabling machines to interpret and understand images and videos with high accuracy.

Originally rooted in image processing and supervised learning, deep learning now extends across data science fields such as natural language processing, reinforcement learning, and time series analysis.

This course provides a comprehensive introduction to deep learning with a strong focus on computer vision applications.

You will begin with core concepts and methodologies, followed by hands-on implementation of neural networks including multilayer perceptrons and convolutional neural networks (CNNs).

Using TensorFlow and Keras, you will explore hyperparameter tuning, model architectures, and practical training techniques.

The course also introduces advanced deep learning architectures such as recurrent neural networks (RNNs) and generative adversarial networks (GANs), and examines real-world applications including augmented reality, autonomous systems, image classification, and predictive analytics.

You will gain practical experience in building, training, and optimizing deep learning models for computer vision and beyond.

Course Highlights Fundamentals of deep learning and neural network architectures Applications of deep learning in computer vision Implementation and training of neural networks using TensorFlow and Keras Convolutional neural networks (CNN) for image recognition and classification Hyperparameter tuning and model optimization Introduction to DNN, RNN, and GAN architectures Model compression and pruning techniques for edge deployment Course Benefits Build a strong foundation in deep learning for computer vision Gain hands-on experience training and fine-tuning neural networks Apply deep learning techniques to real-world image and video problems Learn how to optimize models for deployment on mobile and edge devices Prepare for advanced studies and careers in AI and computer vision Credit earned may be applied toward an academic degree or professional credential, subject to the approval of the receiving institution(s) Course Details and Next Steps Course typically offered: Online during our Winter and Summer academic quarters Prerequisites: Basic proficiency in programming, college calculus, linear algebra, and probability and statistics Next steps: You can continue learning by following Machine Learning Methods or Technical Aspects of Artificial Intelligence certificate program Contact: For more information about this course, please email unex-techdata@ucsd.edu