Deep Learning with R Cookbook

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Tackle the complex challenges of building end-to-end deep learning models with modern R libraries. Understand the intricacies of R deep learning packages to perform various tasks and implement techniques for real-world applications. Explore state-of-the-art methods for fine-tuning neural network models. This resource guides you through the evolution of deep learning, covering advancements like generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning, using R 3.5.x. It begins with an overview of DL techniques applicable to your applications, providing unique recipes to address binomial and multinomial classification, regression, and hyperparameter optimization. Gain hands-on experience with recipes for convolutional neural networks (CNNs), recurrent neural networks (RNNs), Long short-term memory (LSTMs), sequence-to-sequence models, and reinforcement learning. Learn about high-performance computation with GPUs and parallel capabilities in R, along with libraries like MXNet for GPU computing and advanced DL. The book also covers NLP, object detection, and action identification, and teaches how to use pre-trained models in DL applications. By the end, you'll have comprehensive knowledge of deep learning and its packages, enabling you to develop effective solutions for various DL challenges. Ideal for data scientists, machine learning practitioners, researchers, and AI enthusia