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LinkedIn Learning

Training Neural Networks in Python

via LinkedIn Learning

Overview

Take a deep dive into the inner workings of neural networks by learning how to create one from scratch in Python.

Syllabus

Introduction
  • Creating a neural network in Python
  • What you should know
  • Using GitHub for the exercise files
1. Choosing a Neural Network
  • What is a neural network?
  • Why Python?
  • The many applications of machine learning
  • Types of classifiers
  • Types of neural networks
  • Multilayer perceptrons
2. The Building Blocks of Neural Networks
  • Neurons and the brain
  • A simple model of a neuron
  • Activation functions
  • Perceptrons: A better model of a neuron
  • Challenge: Finish the perceptron
  • Solution: Finish the perceptron
  • Logic gates
  • Challenge: Logic gates with perceptrons
  • Solution: Logic gates with perceptrons
3. Building Your Network
  • Linear separability
  • Writing the multilayer perceptron class
  • Challenge: Finish the multilayer perceptron class
  • Solution: Finish the multilayer perceptron class
4. Training Your Network
  • The need for training
  • The training process
  • The error function
  • Gradient descent
  • The delta rule
  • The backpropagation algorithm
  • Challenge: Write your own backpropagation method
  • Solution: Write your own backpropagation method
5. Let's Make a Segment Display Classifier
  • Segment display recognition
  • Challenge: Design your own SDR neural network
  • Solution: Design your own SDR neural network
  • Challenge: Train your own SDR neural network
  • Solution: Train your own SDR neural network
Conclusion
  • Next steps

Taught by

Eduardo Corpeño

Reviews

4.6 rating at LinkedIn Learning based on 113 ratings

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