AI & Machine Learning · Beginner → Expert
🧠 Deep Learning Engineering
Build and train neural networks with modern optimization, CNNs, transformers, sequence models, regularization, evaluation, acceleration and deployment.
Course roadmap
Pass each module exam to unlock the next module.
Module 1 · Beginner
Neural Network Foundations and Automatic Differentiation
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Module 2 · Beginner
Tensors, Batching and Data Pipelines
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Module 3 · Beginner
Loss Functions, Backpropagation and Optimization
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Module 4 · Beginner
Initialization, Normalization and Regularization
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Module 5 · Intermediate
Convolutional Neural Networks and Vision
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Module 6 · Intermediate
Sequence Models, RNNs and LSTMs
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Module 7 · Intermediate
Attention and Transformer Architecture
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Module 8 · Intermediate
Embeddings and Representation Learning
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Module 9 · Advanced
Transfer Learning and Fine-Tuning
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Module 10 · Advanced
Training Stability, Debugging and Experiment Tracking
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Module 11 · Advanced
Evaluation, Calibration and Error Analysis
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Module 12 · Advanced
GPU Acceleration, Mixed Precision and Efficiency
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Module 13 · Expert
Model Compression, Quantization and Distillation
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Module 14 · Expert
Serving, Inference and Deployment
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Module 15 · Expert
Monitoring, Drift and Responsible Deep Learning
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Module 16 · Expert
Expert Capstone: Production Deep Learning System
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Certification gate
Final course exam
Pass mark: 80%. Time limit: 360 minutes. All module exams must be passed first.
Locked until modules are passed