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Learning
Linear Algebra Essentials for Neural Networks → Deploy a Trained Neural Network
A structured, hands-on path from the mathematical foundations of neural computation to building, training, and evaluating modern deep learning models. Learners begin with the perceptron and the backpropagation algorithm, implement networks from scratch, then move to convolutional and recurrent architectures, regularization and optimization techniques, attention and transformer models, and finally transfer learning and deployment. Each node is a self-contained, teachable unit with practical implementation work in Python and PyTorch. Prerequisites are ordered by dependency so that no concept is used before it is introduced.
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18 steps · 4 stages. Click any step to inspect it and see it on the Path Map.
Curated materials referenced by this learning path.
Comprehensive textbook covering linear algebra, probability, feedforward networks, regularization, optimization, convolutional networks, recurrent networks, and more.
Open resource →Free online book that builds neural networks from the perceptron through backpropagation with clear derivations and code examples.
Open resource →Official PyTorch tutorials covering tensors, autograd, nn.Module, training loops, CNNs, RNNs, and transformers.
Open resource →Stanford course notes and assignments on CNNs, training dynamics, regularization, and visual recognition.
Open resource →The original transformer paper introducing scaled dot-product attention, multi-head attention, and the encoder-decoder architecture.
Open resource →Visual walkthrough of the transformer architecture, attention mechanisms, and positional encodings.
Open resource →Hands-on course on using and fine-tuning pretrained transformer models for NLP tasks.
Open resource →