Introduction to Deep Learning Using R

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Introduction to Deep Learning Using R

Introduction to Deep Learning Using R

Introduction   

     More advanced computing techniques are becoming more common as a result of hardware developments and the emergence of big data. This drive has also been driven by businesses looking to use their resources more effectively and rising consumer demand for better products. The field of machine learning has lately experienced a resurgence in interest that has been widely discussed in reaction to these market factors. Machine learning is the study and creation of algorithms that intentionally improve their own behavior in an iterative fashion at the intersection of statistics, mathematics, and computer science.

What is Deep Learning?

    Machine learning can be thought of as a subset of deep learning. It is a field that relies on studying computer algorithms to learn and advance on its own. Deep learning uses artificial neural networks, which are created to mimic how humans think and learn, whereas machine learning uses simpler principles. Up until recently, the complexity of neural networks was constrained by computational capacity. Larger, more complicated neural networks are now possible thanks to developments in big data analytics, which enables computers to watch, learn, and respond to complex events more quickly than people. Speech recognition, language translation, and image categorization have all benefited from deep learning. Any pattern recognition issue may be resolved with it without the need for human interaction.

Learn SQL with practice exercises 

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Contents at Glance

Chapter 1: Introduction to Deep Learning 

Chapter 2: Mathematical Review

Chapter 3: A Review of Optimization and Machine Learning 

Chapter 4: Single and Multilayer Perceptron Models

Chapter 5: Convolutional Neural Networks (CNNs)

Chapter 6: Recurrent Neural Networks (RNNs)

Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks

Chapter 8: Experimental Design and Heuristics

Chapter 9: Hardware and Software Suggestions

Chapter 10: Machine Learning Example Problems

Chapter 11: Deep Learning and Other Example Problems

Chapter 12: Closing Statements 

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