Deep Learning Basics
Understand neural networks, CNNs, RNNs, transformers, backpropagation, and transfer learning. No-code deep learning course with certificate.
What You'll Learn
- Explain how neural networks process data through layers, weights, and activation functions
- Describe how backpropagation and gradient descent train a neural network
- Compare the four main architectures — feedforward, CNN, RNN, and transformer — by use case
- Apply regularization techniques (dropout, batch normalization) to prevent overfitting
- Evaluate when to use transfer learning vs training from scratch
- Design a learning path for a deep learning career based on current market demands
Course Syllabus
Related Skills
Frequently Asked Questions
Do I need to know machine learning first?
Basic ML knowledge helps (what supervised learning is, what training data means) but isn't required. We explain the necessary ML concepts as we go. If you've completed our Machine Learning Fundamentals course, you're well-prepared.
Is there coding in this course?
No coding required. We explain concepts visually with analogies and diagrams. You'll understand how deep learning works and when to use each architecture without writing code.
What math do I need?
None. We explain concepts like gradient descent and backpropagation using analogies, not equations. If you understand 'minimize errors by adjusting settings,' you have enough math for this course.
How does this differ from the Generative AI course?
Generative AI focuses on tools you use (ChatGPT, Midjourney, prompt engineering). This course explains the technology underneath — how neural networks learn, what CNNs and transformers actually do, and how models are trained.