courseoutline_metadesc.tpl

Deep Learning in Theory and Practice (HU0F0S) – Details

Detaillierter Kursinhalt

Module 1 A Gentle Introduction to Deep Learning
  • History of deep learning • Ethics in AI
  • Overview of deep learning
  • A single neuron
  • What is a transfer function?
Module 2 Introduction to TensorFlow
  • Introduction to TensorFlow
  • The TensorFlow architecture
  • TensorFlow data
Module 3 Introduction to Keras
  • Introduction to Keras
  • The Keras architecture
  • Keras models
  • Keras sequential vs functional API
  • Keras layers
  • Keras core modules
Module 4 Overfitting and Underfitting
  • Overfitting and underfitting
  • How to avoid
Module 5 Activation, Loss and Optimizer Functions
  • Activation functions
  • Loss functions
  • Optimization functions
Module 6 Regularizing a Model & Hyperparameter Optimization
  • Why regularize?
  • Regularization types
  • Hyperparameters
  • Optimization techniques
Module 7 Pooling and Convolutions
  • Convolutions
  • Pooling in neural networks
Module 8 Big Data Deep Learning
  • The big data perspective
  • The big data deep learning team and roles
  • Apache Spark
  • Databricks
  • Determined AI
  • HPE Ezmeral