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Deep Learning: Getting Started

Deep Learning: Getting Started

1h 14mIntermediate2024-01-19

Authors

Kumaran Ponnambalam

Kumaran Ponnambalam

Working with data for 20+ years

Course details

Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts and build models quickly. This course aims to provide a simplified path to studying the basics of deep learning and becoming productive quickly. Instructor Kumaran Ponnambalam starts off with an intro to deep learning, including artificial neural networks and architectures. He navigates through various building blocks of neural networks with simple and easy to understand explanations. Kumaran also builds code in Keras to implement these building blocks. He then pulls it all together with an end-to-end exercise. Finally, test what you learned with a deep learning problem and compare your solution with Kumaran’s.

Skills covered

KerasNeural Networks and Deep LearningMachine LearningPythonArtificial Intelligence (AI)Open SourceOne-Off

Concepts

Introduction

  • Getting started with deep learning
  • Prerequisites for the course
  • Setting up the environment

Introduction to Deep Learning

  • What is deep learning
  • Linear regression
  • An analogy for deep learning
  • The perceptron
  • Artificial neural networks
  • Training an ANN

Neural Network Architecture

  • The input layer
  • Hidden layers
  • Weights and biases
  • Activation functions
  • The output layer

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

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

Deep Learning Example 2

  • Spam classification problem
  • Creating text representations
  • Building a spam model
  • Predictions for text

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

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