TensorFlow is a powerful library for doing large-scale numerical computation. One of the tasks at which it excels is implementing and training deep neural networks. In this session we will learn about:
- TensorFlow's Dataflow Programming Paradigm: A TensorFlow model creates a directed graph of various computations. In this part we will talk about the pros and cons of this paradigm, which will help you understand the use cases for TensorFlow.
- TensorFlow 101: This section will introduce different APIs available in TensorFlow to implement Convolutional Neural Networks, Sequence to Sequence models and Digital Signal Processing algorithms.
- Software Development Workflow: This part will talk about how to structure a deep learning project and use Docker.
- Profiling your code: In this section you will learn how to profile your code and optimize it to make it run faster.
- Multi-GPU execution: This part will go over distributed training in TensorFlow.
- Inference Pipeline: Lastly, we will show how to deploy your models in mobile devices like Android and the NVIDIA Jetson TX2.