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Advanced Engineering Course

Deep Learning Fundamentals

Deep Learning Fundamentals builds a solid understanding of how neural networks learn, represent information, and power modern AI systems. Through intuitive explanations and real examples, you’ll uncover the mechanisms behind training, optimization, convolution, and representation learning—without getting lost in unnecessary math. This course requires only basic high school math (optional but helpful) and a curiosity about intelligent systems.

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Deep Learning Fundamentals

Course Syllabus

Preview selected lessons. Full access available after enrollment.

01

Basics

Motivation - Why Neural Networks
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Activation Layers - Why Neural Networks Need Non-Linearity
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Forward Pass in Neural Network
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Backpropagation
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Gradient Descent Variants
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Epoch, Batch, and Iterations
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02

Feature_extraction

Feature Extraction - Traditional ML vs Deep Learning
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Feature Extraction in Computer Vision
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Feature Extraction in NLP
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03

Generalization_regularization

Underfitting and Overfitting
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L1 and L2 Regularization
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Dropout
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Early Stopping
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