Feb-28-2019, 06:12 AM
Packt has recently published a video, Dynamic Neural Network Programming with PyTorch, demonstrates how you can train your networks faster with PyTorch.
Deep learning influences key aspects of core sectors such as IT, finance, science, and many more. Problems arise when it comes to getting computational resources for your network. You need to have a powerful GPU and plenty of time to train a network for solving a real-world task.
What you will learn?
The Author
Anastasia Yanina is a Senior Data Scientist with around 5 years' experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.
Deep learning influences key aspects of core sectors such as IT, finance, science, and many more. Problems arise when it comes to getting computational resources for your network. You need to have a powerful GPU and plenty of time to train a network for solving a real-world task.
What you will learn?
- Get familiar with PyTorch fundamentals while learning to code a deep neural network in Python
- Create any task-oriented extension very quickly with the easy-to-use PyTorch interface
- Perform image captioning and grammar parsing using Natural Language Processing
- Use a computational graph and run it in parallel in the target GPU
- Create unique C++/CUDA extensions for PyTorch that work on CPU and GPU
- Use powerful toolkits from Python library while solving NLP or image recognition tasks
The Author
Anastasia Yanina is a Senior Data Scientist with around 5 years' experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.