Introduction to Deep Learning

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Section 1: Introduction to Deep Learning

  1. What is Deep Learning
  2. Why Deep Learning
  3. History of Deep Learning
  4. Applications of Deep Learning
  5. Overview of Deep Learning Frameworks

Section 2: Building Blocks: Perceptron

  1. Weighted Sum Operation
  2. Mathematical Representation

Section 3: Building Blocks: Activation Functions

  1. Introduction to Activation Functions
  2. Threshold Step Function
  3. Logistic Sigmoid Function
  4. Rectified Linear Unit Function
  5. Hyperbolic Tangent Function

Section 4: Building Blocks: Optimization

  1. Introduction to Deep Neural Networks
  2. Gradient Descent Optimization
  3. Gradient Descent Update Rule
  4. Variants of Gradient Descent

Section 5: Building Blocks: Backpropagation

  1. Backpropagation Intuition
  2. Getting Matrix Dimensions Right
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