Free to read ยท Three levels
Deep Learning: representations, gradients, and reliable experiments
A conceptual course in neural representations, stable objectives, optimization, convolution, sequences, attention, adaptation, and experimental acceptance.
Start learningIn this course
Build a precise mathematical understanding of deep learning before relying on a framework. Eighteen developed lessons connect tensor contracts, nonlinear representations, stable losses, the chain rule, optimization, regularization, architectural assumptions, confidence, adaptation, numerical precision, reproducibility, and acceptance evidence. Every lesson includes two explained calculations, three diagnostic mistakes, and two worked exercises.
All executable examples are tiny deterministic Python standard-library programs using supplied synthetic values. They require no numerical package, neural framework, model download, external dataset, GPU, service, credential, or network connection. The examples establish local arithmetic and contract behavior, not trained-model quality. Separate PyTorch and TensorFlow courses address framework APIs; the MLOps course addresses operational lifecycle practices.
What you will learn
- Derive and check small forward, loss, gradient, and update calculations.
- Explain the assumptions of convolution, recurrence, attention, normalization, and residual paths.
- Preserve valid masking, objective weighting, and schedule clocks.
- Evaluate embedding objectives, adaptation, calibration, and robustness within their evidence limits.
- Define precision, checkpoint, inference, and acceptance contracts that framework implementations can satisfy.
Your learning path
Three levels
Tensor and learning foundations
Shapes, nonlinearities, losses, differentiation, optimization, and generalization.
- Tensors, axes, and batches
- Affine maps, nonlinearities, and representation
- Outputs, logits, and stable losses
- Chain rule and reverse accumulation
- Gradient descent and numerical checks
- Generalization, regularization, and honest selection
Architectures and training dynamics
Initialization, normalization, batching, convolution, sequences, attention, and diagnosis.
- Initialization, normalization, and residual paths
- Minibatches, accumulation, and schedules
- Convolution, padding, and receptive fields
- Sequences, recurrence, and padding
- Attention, masks, and position
- Diagnosing training dynamics
Representation and acceptance evidence
Embedding objectives, calibration, adaptation, precision, reproducibility, and acceptance.
- Embeddings and contrastive objectives
- Calibration, shift, and robustness
- Transfer, freezing, and constrained adaptation
- Precision, memory, and efficiency
- Reproducibility and checkpoint contracts
- Ablations and inference acceptance
Who this is for
- Developers and analysts who want to reason about neural-network mathematics and experiments.
- Learners preparing for framework-specific deep-learning implementation.
Before you start
- Python functions, lists, loops, assertions, and basic exception handling.
- Vectors, matrices, derivatives, means, variance, and elementary probability.
- Machine-learning concepts including held-out evaluation, leakage, baselines, and model selection.