Introduction to Deep Learning Using R

Chapter 1: What is Deep Learning?
Chapter Goal: Review the history of Deep Learning, how where the field is today, and discuss the general goals that the book has for the reader in their progression.
No of pages 10

Chapter 2: A Review of Notation, Vectors and Matrices
Chapter Goal: Establish a sense of understanding in the aforementioned topics within the reader to allow them to understand the models described later. Topics discussed includes the following: Notation, vectors, matrices, inner products, norms, and linear equations.
No of Pages: 50

Apress Proposal Submission Form for Prospective Authors
Chapter 3: A Review of Optimization
Chapter Goal: Discuss/Review Optimization concepts and how it is used in Deep Learning models. Topics discussed include the following: constrained and unconstrained optimization, gradient descent, and newton's method.
No of pages: 60

Chapter 4: Single Layer Artificial Neural Network (ANNs)
Chapter Goal: Introduce readers to ANNs, it's uses, the math that powers the model, as well as discussing its limitations
No of pages: 10

Chapter 5: Deep Neural Networks (Multi-layer ANNs)
Chapter Goal: Establish the difference between single and multilayer ANNs as well as discuss the nuances that are created as a product of having multiple hidden layers
No of pages: 10

Chapter 6: Convolutional Neural Networks (CNNs)
Chapter Goal: Build upon the knowledge of neural networks described earlier and begin to branch in the other models, such as CNNs. Here, we will establish what a convolutional layer is, in addition to what the uses of this model are, such as computer vision and processing visual data.
No of pages: 10

Apress Proposal Submission Form for Prospective Authors
Chapter 7: Recurrent Neural Networks (RNNs)
Chapter Goal: Describe the mathematics and intuition behind RNNs and their use cases, such as handwriting recognition and speech recognition. Also describe how the unique structure behind them differentiates themselves from feed forward networks.
No of pages: 10

Chapter 8: Deep Belief Networks and Deep Boltzman Machines
Chapter Goal: Discuss the similarities between these two models and how their disadvantages and advantages in contrast to the prior Deep Learning Models described
No. of pages: 20

Chapter 9: Tuning and Training Deep Network Architectures
Chapter Goal: Establish an understanding of how to properly train Deep Network models and tune their parameters as to avoid common pitfalls such as overfitting.
No. of Pages: 20

Chapter 10: Experimental Design and Variable Selection
Chapter Goal: Now that the reader has an understanding of various Deep Learning Models, and the concepts that power them, it is time to establish an understanding of how to properly perform experiments, including the examples given in the later part of the text. Topics discussed include the following: Fisher's priciples, Plackett-Burman designs, statistical control, and variable selection techniques.
About the Author: Taweh Beysolow II is a Machine Learning Scientist currently based in the United States with a passion for research and applying machine learning methods to solve problems. He has a Bachelor of Science degree in Economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. Currently, he is extremely passionate about all matters related to machine learning, data science, quantitative finance, and economics.