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CodeSignal

Neural Networks

via CodeSignal

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Overview

Dive deep into the theory and implementation of Neural Networks. This course will have you implementing tools at the heart of modern AI such as Perceptrons, activation functions, and the crucial components of multi-layer Neural Networks. All of this without the help of high-level libraries leaves you with a profound understanding of the underpinning mechanisms.

Syllabus

  • Unit 1: Building a Perceptron Model
    • Building a Perceptron for AND Logic Gate Simulation
    • Debugging Perceptron Summation Calculation
    • Perceptron Activation Logic Simulation
  • Unit 2: Activation Functions in C++
    • Implementing the Step Function in C++
    • Visualizing Sigmoid and Tanh Functions
    • Visualizing ReLU and Softplus Functions
  • Unit 3: Backpropagation in Neural Networks
    • Neural Network Training for XOR Problem
    • Neural Network Prediction Implementation
    • Neural Network Weight Update Implementation
    • Neural Network Enhancement Task

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