AI / MLWeak
Deep Learning
Deep Learning covers neural network architectures, backpropagation, activation functions, and optimisation. This is currently your largest gap — foundational concepts are partially understood but require significant reinforcement.
26%mastery
Difficulty
Advanced
Estimated Time8weeks — ~10 hrs/wk
Total Hours80hours of study
Practice Readiness26%
Why It Matters
Deep learning is responsible for nearly every AI breakthrough of the past decade — from image recognition to large language models. It is the step from classical ML to the frontier of modern AI research.
Topics Included
1Artificial Neural Networks
2Backpropagation
3Activation Functions
4Batch Normalisation
5Dropout & Regularisation
6CNNs
7RNNs & LSTMs
8Optimisers (Adam, SGD)
Applications
Image classification and segmentation
Speech recognition
Natural language generation
Game playing agents
Prerequisites
Used In
NLP
Computer Vision
Robotics
Machine Learning
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Your Next Step
Implement a neural network from scratch in NumPy to solidify the backpropagation algorithm.