Introduction to Deep Learning
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Section 1: Introduction to Deep Learning
- What is Deep Learning
- Why Deep Learning
- History of Deep Learning
- Applications of Deep Learning
- Overview of Deep Learning Frameworks
Section 2: Building Blocks: Perceptron
- Weighted Sum Operation
- Mathematical Representation
Section 3: Building Blocks: Activation Functions
- Introduction to Activation Functions
- Threshold Step Function
- Logistic Sigmoid Function
- Rectified Linear Unit Function
- Hyperbolic Tangent Function
Section 4: Building Blocks: Optimization
- Introduction to Deep Neural Networks
- Gradient Descent Optimization
- Gradient Descent Update Rule
- Variants of Gradient Descent
Section 5: Building Blocks: Backpropagation
- Backpropagation Intuition
- Getting Matrix Dimensions Right