1. introduction to deep learning

• What are neural networks and how do they learn?
• Mathematical basics explained in compact form
• Neural networks with Keras and TensorFlow
• Models: evaluation and adaptation
• Models: use and storage

2. data preparation and feature extraction

• Data preparation with Pandas
• Exploratory data analysis
• Standardization of numerical data and text data
• Feature extraction: Extracting features from data
• Train networks with small amounts of data

3. specialized neural networks

• Convolutional neural networks (CNN)
• Updating weights for CNNs
• Max pooling and dropout
• Monitor teach-in processes with TensorBoard
• Recurrent neural networks (RNN)
• Time series analysis and text processing with RNN

4. deploy models and transfer learning

• Use of cloud GPUs for machine learning projects
• Introduction to transfer learning and the Zoo model
• Presentation of ImageNet, ResNet, VGG16
• Use pre-trained layers in your own projects