Deep Learning
Introduction
Your first deep learning model
Create a code environment with the required packages
Create a Deep Learning analysis to solve a Prediction problem
Review the architecture of you Deep Learning model
Monitor the performance of your model during the training
Model architecture
Build Keras model
input_shapes
n_classes
Layer dimensions
Compile the model
Training
Multiple inputs
Regular multi-feature inputs
Custom-processed single-feature inputs
Using image features
Scoring images
Using text features
Runtime and GPU support
Code environment
Selection of GPU
Using multiple GPUs for training
Advanced topics
Start with weights from a previously trained model
How is the model trained?
Advanced training mode
Build sequence
Fit model
Usage of metrics in Callbacks
Troubleshooting
Using pre-trained models from Keras
Code environment lineage
TensorFlow session
ML API
Number of outputs in the model
Enforced code environment for Project