Machine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns, which are otherwise invisible to us.

Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The course begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you’ll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN).

By the end of this course, you’ll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems.

• Detect a variety of data problems to which you can apply deep learning solutions
• Learn the PyTorch syntax and build a single-layer neural network with it
• Build a deep neural network to solve a classification problem
• Develop a style transfer model
• Implement data augmentation and retrain your model
• Build a system for text processing using a recurrent neural network

For an optimal student experience, we recommend the following hardware configuration:

• Processor: 2.6 GHz or higher multi-core processor
• Memory: 8GB RAM
• Hard disk: 35GB or more

You’ll also need the following software installed in advance:

• OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
• Browser: Google Chrome/Mozilla Firefox Latest Version
• Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter notecourse on your browser)
• Python 3.4+ (latest is preferable) installed (from https://python.org)
• Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so)