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. You’ll 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. You’ll 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, you’ll learn to build smart systems by leveraging the power of Neural Networks and implement cutting-edge CNN architectures.

Contents and Overview

This 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, you’ll learn to build smart systems by leveraging the power of Neural Networks and implement cutting-edge CNN architectures.

About the Authors

• Arun 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. Guest Lecturer at Community Colleges. Data Science in different domains o Fintech (Lending Club), o Cybersecurity (VISA) o Advertising Technology (Yahoo / Microsoft) o Bot Technology (voicy .ai) o Retail (WRS) o IOT (GE) o ERP (SAP) o Health Care (Blue Cross).

• Anshul Srivastav is a global technology leader who has been instrumental in driving technology transformations for business revenues in the range of multi-Billion USD. His experience has been in taking up strategic technology initiatives, architecting, delivering, and managing them at an enterprise level. Anshul has several notable career accomplishments, wherein he has led, created, and launched key e-commerce, mobile, and business intelligence initiatives for the world's #1 insurance brand AXA, in the fastest growing emerging markets of Asia. He is currently in a leadership role as the Chief Information Officer Information Technology and Digital Officer, leading the IT Strategy, Technology Transformations, Analytics, software delivery, architecture and Cloud for Union Insurance (UAE, Oman and Bahrain) across all lines of business (Life, General (P&C) and Health Insurance) Creating and Driving big strategic Initiatives aligning IT transformation to deliver business value. Major Cloud transformations impacted the bottom line of Union by multimillion AED in the first year. Transformation on Digital added multimillion revenues in Life, P&C and Health lines of business. Machine Learning, Deep Learning, and Robotic Process Automation are some key business transformations implemented recently. He is a transformational leader and Senior Management IT professional, with almost two decades of experience, spread across multiple geographies (US, Europe, South East Asia, and the Middle East). Anshul has built and led local, regional, and global teams across 3 continents, and capitalized on opportunities to drive revenues, profits, and growth. Strong P&L management. Anshul has been mentoring startups, management students (IIM Bangalore), and incubators/accelerators such as Astrolabs Dubai, T Labs, CH9, Flat6labs, and other incubators since 2012. Startups mentored are in the tech space of analytics, mobility, tech-based microfinance institutions, healthcare tech, and analytics, and tech-based retail merchandising, logistics and mobile wallets spread across geographies including Singapore, Dubai, and the Middle East, India and Europe. Anshul is a speaker on Blockchain, Internet of Things (IoT), Artificial Intelligence, Machine, and Deep Learning, Digital Transformation, Cloud and Mobility. He won a couple of Star Performer of the Year Awards from AXA India & AXA Asia. Three times he has been awarded the AXA innovation award both at AXA Asia and AXA Group level. CIO of the year award, InfoSec Maestro Award, CSO Next of the year award. CISO award from MESA Dubai. CIO award from CNME Dubai. Anshul writes on innovation, big data, and technology transformations, Blockchain and had a couple of articles published in CNME, Innovation and Tech Middle East, Dataquest, LinkedIn, and PM Network.

• Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a Ph.D. candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc degree in Computer Science. Before joining Fraunhofer FIT, he worked as a Researcher at Insight Centre for Data Analytics, Ireland. Before this, he worked as a Lead Engineer at Samsung Electronics' distributed R&D Institutes in Korea, India, Turkey, and Bangladesh. Previously, he worked as a Research Assistant at the database lab, Kyung Hee University, Korea. He also worked as an R&D engineer with BMTech21 Worldwide, Korea. Before this, he worked as a Software Engineer with i2Soft Technology, Dhaka, Bangladesh. He has more than 8 years' experience in the area of research and development with a solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and Python. He has published several books, articles, and research papers concerning big data and virtualization technologies, such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce. He is also equally competent with deep learning technologies such as TensorFlow, DeepLearning4j, and H2O. His research interests include machine learning, deep learning, the semantic web, linked data, big data, and bioinformatics. Also, he is the author of the following book titles: Large-Scale Machine Learning with Spark (Packt Publishing Ltd.)
  Deep Learning with TensorFlow (Packt Publishing Ltd.)
  Scala and Spark for Big Data Analytics (Packt Publishing Ltd.)

• Mohit Sewak is an Artificial Intelligence scientist with extensive experience and technical leadership in research, architecture, and solution of Artificial Intelligence-driven cognitive and automation products and platforms for industries such as IoT, retail, BFSI, and cybersecurity. In his current role at QiO Technologies, Mohit leads the reinforcement learning initiative for Industry 4.0 and Smart IoT. In his previous role, Mohit was associated with IBM Watson Commerce (Software Group) where he led the research/science initiatives for the Watson Cognitive Commerce line of product features and offerings. Mohit has been the Lead Data Scientist/Analytics Architect for some of the most renowned industry-leading International AI/ DL/ ML software and industry solutions. Mohit is also a thought leader in the field of Artificial Intelligence and Machine Learning and has authored multiple books and scientific publications in this area.

• Pradeep Pujari is a machine learning engineer at Walmart labs and a distinguished member of ACM. His core domain expertise is in information retrieval, machine learning, and Natural Language Processing. In his free time, he loves exploring AI technologies, reading, and mentoring.