Deep Learning Fundamentals

At the end of the course, you will earn a Certificate of Completion from Spirit in Projects.

Do you enjoy analytical thinking, are fascinated by machines that can learn, and therefore want to explore the potential of neural networks and especially Deep Learning? If you want to learn everything about current developments in the field of Deep Learning, then look forward to this training. Together we will train systems that learn tasks to execute them automatically afterwards.

Advising

Objectives

  • Understand Machine Learning methods
  • Learn the structure and functioning of neural networks
  • Apply Deep Learning for training neural networks
  • Understand and deploy application areas such as pattern recognition, image and text processing, and object recognition
  • Use tools and platforms for Deep Learning

Target groups:

Business Analyst, Requirements Engineer, Usability Expert, Scrum Master, AI Expert, Project Manager, Project Lead, Demand Manager, Portfolio Manager, IT Project Manager, Test Manager, Tester, Test Automation Specialist, Test Engineer, Enterprise Architect, System Architect, Software Architect, Software Designer, Software Developer, Product Owner and anyone who wants to engage with artificial intelligence.

Show dates and times

Syllabus

1. Fundamentals

  • What is Machine Learning?
  • The difference between Machine Learning, Artificial Intelligence, and Data Science

2. Introduction to Methodological Foundations

  • Difference from traditional software development
  • Components of Machine Learning

3. Prerequisites for Machine Learning

  • Data requirements
  • Predictability requirements

4. Learning Approaches for Machine Learning

  • Supervised
  • Unsupervised
  • Reinforcement

5. Model Types of Machine Learning

  • Decision Tree
  • Neural Networks
  • Regression analysis

6. Deep Learning

  • What is an artificial neural network?
  • What is Deep Learning?
  • Advantages of Deep Learning
  • Simple deep neural networks
  • Convolutional Neural Networks
  • GAN
  • Transformer

7. ML Platforms and Technologies

  • Google TensorFlow
  • PyTorch
  • Repositories
  • ML programming languages

Advising after course completion

Spirit in Projects