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What You Need Before Deep Learning

Deep learning builds on neural networks with additional architectural complexity. Prepare with these foundations.

6-10 weeks to prepare

9 topics to cover

4 essential

You must understand these before deep learning

4 topics

Deep learning IS neural networks with many layers

gradient descent

All deep learning training uses gradient-based optimization

chain rule

Backpropagation through deep networks requires chain rule mastery

matrices

Tensor operations are generalized matrix operations

Critical for training deep networks successfully

3 topics

adam

Adam is the default optimizer for deep learning

regularization opt

Dropout, weight decay, and other regularization techniques

probability basics

Loss functions, dropout, and probabilistic interpretations

Before specialized architectures

2 topics

convexity

Deep learning is non-convex - understand the challenges

eigenvalues

Gradient flow, initialization, and network dynamics

Once you've covered the essential prerequisites, you'll have a solid foundation for understanding deep learning.