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Deep Learning: Getting Started

Offered By: LinkedIn Learning

Tags

Deep Learning Courses Artificial Intelligence Courses Machine Learning Courses Neural Networks Courses Linear Regression Courses Gradient Descent Courses Activation Functions Courses Backpropagation Courses Perceptron Courses

Course Description

Overview

Learn the basics of deep learning and get up and running with this technology.

Syllabus

Introduction
  • Getting started with deep learning
  • Prerequisites for the course
  • Setting up the environment
1. Introduction to Deep Learning
  • What is deep learning?
  • Linear regression
  • An analogy for deep learning
  • The perceptron
  • Artificial neural networks
  • Training an ANN
2. Neural Network Architecture
  • The input layer
  • Hidden layers
  • Weights and biases
  • Activation functions
  • The output layer
3. Training a Neural Network
  • Setup and initialization
  • Forward propagation
  • Measuring accuracy and error
  • Back propagation
  • Gradient descent
  • Batches and epochs
  • Validation and testing
  • An ANN model
  • Reusing existing network architectures
  • Using available open-source models
4. Deep Learning Example 1
  • The Iris classification problem
  • Input preprocessing
  • Creating a deep learning model
  • Training and evaluation
  • Saving and loading models
  • Predictions with deep learning models
5. Deep Learning Example 2
  • Spam classification problem
  • Creating text representations
  • Building a spam model
  • Predictions for text
6. Deep Learning Exercise
  • Exercise problem statement
  • Preprocessing RCA data
  • Building the RCA model
  • Predicting root causes with deep learning
Conclusion
  • Extending your deep learning education

Taught by

Kumaran Ponnambalam

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