Saleh - Applied deep learning with PyTorch: demystify neural networks with PyTorch

Machine learning is fast becoming the preferred way to solve data problems, thanks to the huge variety of mathematical algorithms that find patterns 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 book 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 book, 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.

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.

• 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

Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this book useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.

Applied Deep Learning with PyTorch takes a practical and hands-on approach, where every chapter has a practical example that is demonstrated end-to-end, from data acquisition to result interpretation. Considering the complexity of the concepts at hand, the chapters include several graphical representations to facilitate learning.

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

• Processor: Intel Core i3 or equivalent