By the end of this course, learners will differentiate core AI concepts, construct deep neural networks, apply image and text models, develop attention-based NLP systems, and design recommender solutions.
Deep Learning: Build & Optimize Neural Networks
Gain insight into a topic and learn the fundamentals.
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Build and optimize deep neural networks using PyTorch.
Apply AI models to vision, NLP, and recommendation tasks.
Implement attention and transformer architectures effectively.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
22 assignments
Taught in English
Recently updated!
November 2025
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There are 6 modules in this course
This module introduces learners to the core principles of machine learning and deep learning, exploring their methods, applications, and the evolution from perceptrons to deep neural networks.
What's included
12 videos3 assignments
This module provides hands-on exposure to essential coding platforms, tools, and frameworks like Jupyter, Google Colab, and PyTorch, while building foundational skills with tensors, gradients, and basic networks.
What's included
15 videos4 assignments
This module explores image classification through practical case studies, guiding learners to preprocess, transform, and visualize datasets, then build, train, and test deep neural networks on benchmarks like MNIST and CIFAR-10.
What's included
18 videos4 assignments
This module introduces natural language processing (NLP) tasks, including text classification with CNNs and text generation with transformers, focusing on preparing textual data, building models, and evaluating results.
What's included
15 videos4 assignments
This module dives deeper into NLP using attention-based architectures, covering sequence-to-sequence models for text translation, encoder-decoder frameworks, and best practices for training and evaluation.
What's included
14 videos4 assignments
This module extends deep learning applications to structured tabular data and recommender systems, demonstrating predictive modeling and approaches like collaborative and content-based filtering.
What's included
7 videos3 assignments
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University of California, Davis
