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.
₹1999₹3499
Enroll NowCourse Syllabus
Preview selected lessons. Full access available after enrollment.
01
Foundations Of Learning Systems
Why Neural Networks Exist (Beyond Traditional ML)
PreviewNon-Linearity: The Source of Intelligence in Neural Networks
PreviewForward Pass: How Neural Networks Transform Data
PreviewBackpropagation: How Networks Learn from Mistakes
PreviewGradient Descent Variants: Learning as Iterative Improvement
PreviewEpochs, Batches, and Iterations: The Rhythm of Learning
Preview02
Learning Representations
03
Learning Beyond The Data
04
Making Learning Efficient
Momentum: Accelerating Learning in the Right Direction
🔒 LockedImproved Momentum: Stabilizing the Learning Process
🔒 LockedAdagrad: Adapting Learning to Parameters
🔒 LockedRMSProp: Controlling Learning Dynamics
🔒 LockedAdam: Combining Momentum and Adaptation
PreviewBatch Normalization: Stabilizing and Accelerating Learning
Preview05
Dynamics Of Learning
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