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University of Colorado Boulder

Introduction to Deep Learning

University of Colorado Boulder via Coursera

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Overview

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Introduction to Deep Learning provides a rigorous, concept-driven introduction to the models that power modern AI systems—from image recognition to large language models. You’ll build neural networks from first principles, understanding how forward passes, loss functions, and backpropagation enable learning. As the course progresses, you’ll train and regularize deep models, design convolutional networks for vision, model sequences with RNNs, LSTMs, and attention, and apply transformer-based architectures such as BERT, GPT, and Vision Transformers. You will also look at the latest trends in contrastive learning and CLIP. By combining mathematical foundations with practical application, this course equips you to understand, train, and use deep learning models with confidence. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

Syllabus

  • Neural Network Foundations
    • Welcome to Introduction to Deep Learning. This module builds the mathematical foundations of neural networks. Starting from linear models, you will learn about the artificial neuron and develop the mathematics of gradient descent and backpropagation. The focus is on understanding how and why neural networks work through the underlying math—covering the forward pass, loss functions, and the chain rule to show how information flows through networks and how they learn from data.
  • Training and Regularizing Neural Networks
    • This module focuses on training neural networks effectively. Topics include optimization algorithms, hyperparameter tuning, and regularization techniques to prevent overfitting and achieve good generalization. You will compare different optimizers like SGD, momentum, and Adam, understand how learning rate and batch size affect training dynamics, and apply weight decay, dropout, early stopping, and batch normalization.
  • Convolutional Neural Networks for Image Recognition
    • This module introduces you to convolutional neural networks (CNNs), the foundation of modern computer vision. Topics include how convolutional and pooling layers work, CNN architecture design, and practical techniques like data augmentation and transfer learning. The module covers classic architectures like VGG and ResNet and explains why CNNs outperform fully-connected networks on image data.
  • Sequence Modeling – RNNs, LSTMs, and the Attention Mechanism
    • This module covers sequence modeling, starting with recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), then progressing to the attention mechanism—the key innovation that led to transformers. Topics include how RNNs maintain hidden states across time steps, why the vanishing gradient problem motivated LSTMs, and how attention allows models to focus on relevant parts of their input.
  • Transformers, Vision Transformers, and CLIP
    • This final module covers the transformer architecture, which has revolutionized deep learning across domains. Topics include BERT and GPT as encoder-only and decoder-only variants, Vision Transformers (ViT) that apply attention to images, and CLIP for multimodal learning connecting vision and language. The module emphasizes applying pre-trained models to real tasks.

Taught by

Nikita Kazeev, Andrei Zimovnov, Alexander Panin, Evgeny Sokolov and Ekaterina Lobacheva

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Reviews

3.7 rating, based on 3 Class Central reviews

Start your review of Introduction to Deep Learning

  • Profile image for Lagom Amine
    Lagom Amine
    1
    This is a very nice course, it's part of the advanced machine learning specialization so it would make sense if the lecturers go fast through some mathematical intricacies. lecturers are not obviously native speakers but who cares, they speak clea…
  • Eleni Beyene
    1
    the best deep learning course i ever took and it makes me so happy, also i am interested in deep learning
  • Anonymous
    Teachers speak fast and not understandable. Concepts are not explained well either. Practicals need better explanation and should be strongly related to what is thought in the course
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