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).

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 course 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.

• Introduction to Deep Learning and PyTorch
• Building Blocks of Neural Networks
• Classification Problem Using DNN
• Convolutional Neural Networks
• Style Transfer
• Analyzing the Sequence of Data with RNNs

• 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

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• Payment by invoice