University of Colorado Boulder

Introduction to Deep Learning

University of Colorado Boulder

Introduction to Deep Learning

本课程是 Machine Learning: Theory and Hands-on Practice with Python 专项课程 的一部分

Daniel E. Acuna

位教师：Daniel E. Acuna

1,531 人已注册

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您将学到什么

  • Explain the mathematical foundations of neural networks and how they learn from data.

  • Train and regularize deep neural networks for effective generalization.

  • Design and apply specialized neural network architectures for images and sequences.

  • Apply transformer-based and multimodal models to real-world scenarios.

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最近已更新！

January 2026

作业

6 项作业

授课语言：英语（English）

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积累特定领域的专业知识

本课程是 Machine Learning: Theory and Hands-on Practice with Python 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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  • 获得对主题或工具的基础理解
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  • 获得可共享的职业证书

该课程共有5个模块

单元详情

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.

涵盖的内容

15个视频6篇阅读材料2个作业1个编程作业

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.

涵盖的内容

7个视频2篇阅读材料1个作业1个编程作业

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.

涵盖的内容

7个视频2篇阅读材料1个作业1个编程作业

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.

涵盖的内容

7个视频1篇阅读材料1个作业1个编程作业

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.

涵盖的内容

8个视频1篇阅读材料1个作业1个编程作业

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位教师

Daniel E. Acuna
University of Colorado Boulder
3 门课程4,552 名学生

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University of Colorado Boulder

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