Neural Networks and Architecture
A structured program that moves from the basic math of activation functions all the way through transformer design — covering both theory and practical implementation at each stage.
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What the program covers
Six modules built around a single thread: how networks learn, and why certain architectures work better for specific problems. Each module builds directly on the previous one, so gaps don't accumulate.
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01
Fundamentals of Neural Networks
Perceptrons, weights, biases, and the geometry of decision boundaries — explained through worked examples before any code is introduced.
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02
Feedforward and Backpropagation
Forward passes, loss computation, gradient flow, and the chain rule applied to real network diagrams — not just formulas on a slide.
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03
Convolutional Network Architecture
Kernel operations, pooling strategies, receptive field calculations, and a dissection of ResNet and EfficientNet design decisions.
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04
Recurrent Networks and Sequence Modeling
Vanilla RNNs, vanishing gradients, LSTM gating mechanics, and where GRUs trade off complexity for speed in practical settings.
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05
Transformer Architecture and Attention
Scaled dot-product attention, multi-head configurations, positional encoding, and a comparison of encoder-only vs. decoder-only layouts. This is also where QBO intuit-style iterative refinement enters the picture for parameter tuning workflows.
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06
Applied Project and Evaluation
Learners pick an architecture problem, implement a solution, and present it for instructor review with structured written feedback provided.
Session formats and pricing
Three ways to work through the material — depending on how much structure and direct instructor time you need.
- All 6 modules
- Video recordings
- Assignment templates
- Live sessions
- All 6 modules
- Live group classes
- Peer discussion board
- Instructor Q&A
- Custom pace
- Dedicated instructor
- Direct code review
- Flexible scheduling
What learners said
Three people who completed the program — different backgrounds, different formats, similar outcome: they could actually build and explain what they'd learned.
The backpropagation module finally clicked for me here. I'd read three textbooks before this and the distinction between theory and actual implementation was always blurred. Here it wasn't.
I came in knowing Python but not much about architecture choices. By module four I was reading papers and understanding why specific design decisions were made. That felt like real progress.
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The transformer module was the most thorough explanation of attention I've seen outside of actual research papers — and far more readable. The applied project at the end made the whole thing concrete.