Learn the fundamentals of CNNs, RNNs, and Transformers for modern deep learning applications.
What You'll Learn
- Understand CNN architectures for image tasks
- Learn RNNs, LSTMs, and sequence modeling
- Explore Transformers and self-attention
- Build models using deep learning frameworks
- Apply preprocessing to text and image datasets
Requirements
- Python
- basic ML knowledge
Earn Your Certificate
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Course Curriculum
4 Chapters
20 Lectures
32h 45m 55s
1. Convolutional Neural Networks (CNNs)
5 Lectures|1 Assignment|1 Quiz|5h 24m
1. Building a Simple CNN Model
2. CNN Architectures & Use Cases
3. Pooling, Padding & Strides Explained
4. What Are CNNs? Core Principles
5. Convolution, Filters & Feature Extraction
6. CNN Basics
7. Build CNN Model
8. CNN Cheatsheet PDF
2. RNN, LSTM & GRU Fundamentals
5 Lectures|16h 56m
3. Transformers & Attention
5 Lectures|1 Assignment|1 Quiz|7h 2m 55s
4. Comparing CNNs, RNNs & Transformers
5 Lectures|1 Assignment|3h 23m
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$110.00
Course Includes
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