# Deep Learning[¶](https://doc.dataiku.com/dss/latest/machine-learning/deep-learning/index.html#deep-learning "Permalink to this headline")

* 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
