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Working with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are the most popular and widely used deep neural networks for computer vision problems. They are used in a variety of applications including image classification, face recognition, document analysis, medical image analysis, action recognition, and natural language processing. In this chapter, we will focus on learning convolutional operations, and concepts such as padding and strides, to optimize CNNs. The idea behind this chapter is to make you well versed with the functioning of the CNN and learn techniques such as data augmentation and batch normalization to fine-tune your network and prevent overfitting. We will also provide a brief discussion about how we can leverage transfer learning to boost model performance.

In this chapter, we will cover the following recipes:

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Description

Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.

What you will learn

Work with different datasets for image classification using CNNs Apply transfer learning to solve complex computer vision problems Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization Build deep generative models to create photorealistic images using GANs and VAEs Use MXNet to accelerate the training of DL models through distributed computing

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