PyTorch Deep Learning in 7 Days
What you'll learn
- Get comfortable with the most used PyTorch concepts, modules and API including Tensor operations, data representations, and manipulation
- Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers
- Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing
- Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems
- Implement state of the art in Natural Language Processing to solve real-world problems such as sentiment analysis
- Implement a simple Generative Adversarial Network to generate fancy images after training on a large image dataset
PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has the ability to create dynamic Neural Networks on CPUs and GPUs, both with a significantly less code compared to other competing frameworks.
PyTorch has a unique interface that makes it as easy to learn as NumPy. This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code.
This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advance concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. By the end of the course, you will be able to build Deep Learning applications with PyTorch.
Target Audience
This course is for software development professionals and machine learning enthusiasts, who have heard the hype of Deep Learning and want to learn it to stay relevant in their field. Basic knowledge of machine learning concepts and Python programming is required.
Business Outcomes
-
A systematic guide on Deep Learning to help you build smart applications
-
Cover core concepts and architectures of Deep Learning systems without getting bogged down in mathematical notation
-
Solve Machine Learning problems by applying Deep Learning architectures
