You now have deep learning foundations. You can explain forward and backward propagation, choose activation functions, and discuss optimization techniques.
You understand why vanishing gradients plague deep networks and how residual connections help. You know why CNNs work for vision and why RNNs were replaced by transformers.
These concepts are prerequisites for understanding modern LLMs. Next phases will cover transformers, LLMs, and the GenAI techniques that dominate current interviews.