Deep Learning for computer vision

Modern computer Vision utilized highly complex Deep Learning models to achieve outstanding performances. In this workshop we train you on how to develop these systems to fit your individual challenges.

Deep Learning for computer vision

What you will learn

Introduction in Deep Learning

  • Understanding of the terms Machine Learning, Deep Learning and Artificial Intelligence
  • Use Cases and success stories
  • Data sets and typical tasks
  • Data driven work instead of classic model building
  • Planning of successful Deep Learning projects

Deep Learning basics

  • Structuring of Deep Learning tasks
  • Loss Functions and gradient based optimization
  • Evaluation of deep learning models
  • From linear regression to machine learning models
  • Hidden layers and activation functions

Convolution neural networks in TensorFlow

  • Convolution neural networks vs multilayer perceptron
  • Convolution und pooling operations
  • Implementation and training of CNNs

Introduction to TensorFlow

  • System setup and installation
  • Introduction in the programming of TensorFlow
  • Execution of computational graphs

Deep Learning in TensorFlow

  • Structuring of Deep Learning projects
  • Data configuration and preprocessing
  • Implementation of neural networks
  • Training and evaluation of neural networks

Advanced Topics in Deep Learning

  • Hyperparameter tuning and regularization
  • Batch normalization and activation functions
  • Data augmentation
  • Overview of the most important network architecture