This chapter focuses on technical solutions to set up popular deep learning frameworks. First, we provide solutions to set up a stable and flexible environment on local machines and with cloud solutions. Next, all popular Python deep learning frameworks are discussed in detail:
- Setting up a deep learning environment
- Launching an instance on Amazon Web Services (AWS)
- Launching an instance on Google Cloud Platform (GCP)
- Installing CUDA and cuDNN
- Installing Anaconda and libraries
- Connecting with Jupyter Notebook on a server
- Building state-of-the-art, production-ready models with TensorFlow
- Intuitively building networks with Keras
- Using PyTorch's dynamic computation graphs for RNNs
- Implementing high-performance models with CNTK
- Building efficient models with MXNet
- Defining networks using simple and efficient code with Gluon
