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One stop guide to implement cutting-edge CNN architectures and build Neural Network models to solve complex problems!
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R: Neural Nets and CNN Architecture in R – Masterclass!
Neural networks are one of the most fascinating Machine Learning models for solving a wide range of complex computational problems efficiently in different areas of AI! Moreover, Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative e-commerce, and more! Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution. This course will help you create innovative solutions around image and video analytics to solve complex Machine Learning- and computer vision-related problems and implement real-life CNN models. This comprehensive 3-in-1 course is a step-by-step guide to understanding Neural Networks with R with concise and illustrative examples explaining core ConvNet concepts to help you understand, implement and deploy your CNN models quickly. Youll start off by learning how to build and train neural network models to solve complex problems. Implement solutions from scratch, covering real-world case studies to illustrate the power of neural network models. Youll also apply supervised and unsupervised learning to your daily projects. Finally, implement CNN models on image classification, transfer learning, object detection, instance segmentation, GANs, and more By the end of the course, youll learn to build smart systems by leveraging the power of Neural Networks and implement cutting-edge CNN architectures. Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Getting Started with Neural Nets in R, covers building and training neural network models to solve complex problems. This course explains the niche aspects of neural networking and provides you with a foundation from which to get started with advanced topics by implementing them in R. This course covers an introduction to neural nets, the R language, and building neural nets from scratch- with R packages; specific worked models are applied to practical problems such as image recognition, pattern recognition, and recommender systems. At the end of the course, you will learn to implement neural network models in your applications with the help of practical examples from companies using neural nets. The second course, Deep Learning Architecture for Building Artificial Neural Networks, covers an introduction to deep learning and its architectures with real-world use cases. The course starts off with an introduction to Deep Learning and the different tools, hardware, and software before we begin to understand the different training models. We then get to what everyone is talking about: Neural Networks. Here we understand how Neural Networks work and the benefits they offer for supervised and well as unsupervised learning before building our very own neural network. We will then move on to understanding the different Deep Learning Architectures, including how to set up your architecture and align the output. Finally, we take a look at Artificial Neural Networks and understand how to build your own ANN. The third course, Practical Convolutional Neural Networks, covers tackling all CNN-related queries with this fast-paced guide. You will learn to create innovative solutions around image and video analytics to solve complex machine learning- and computer vision-related problems and implement real-life CNN models. This course starts with an overview of deep neural networks using image classification as an example and walks you through building your first CNN: a human face detector. You will learn to use concepts such as transfer learning with CNN and auto-encoders to build very powerful models, even when little-supervised training data for labeled images are available. Later we build upon this to build advanced vision-related algorithms for object detection, instance segmentation, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this course, you should be ready to implement advanced, effective, and efficient CNN models professionally or personally, by working on a complex image and video datasets. By the end of the course, youll learn to build smart systems by leveraging the power of Neural Networks and implement cutting-edge CNN architectures. About the AuthorsArun Krishnaswamy has over 18 years of experience with large datasets, statistical methods, machine learning and software systems. He is one of the First Hadoop Engineers in the world, Advisor to AI Startups. He has 15+ years experience using R. He is also a Ph.D. in Statistics/Math with MS in CS. Expertise in Machine Learning, Neural Nets, and Deep Learning. Deep Experience in AWS, Spark, Cassandra, MongoDB, SQL, NoSQL, Tableau, R, Visualization. Data Science Mentor at UC Berkeley, Stanford, Caltech. Gues
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