Practical Neural Networks & Deep Learning In R

YOUR COMPLETE GUIDE TO PRACTICAL NEURAL NETWORKS & DEEP LEARNING IN R: This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.Among other things:You will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNNs), convolution neural networks (CNNs) and recurrent neural networks (RNNs). You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for c