R: Artificial Neural Nets in R - Beginner to Expert!: 3-in-1

R: Artificial Neural Nets in R - Beginner to Expert!: 3-in-1

https://www.udemy.com/course/r-artificial-neural-nets-in-r-beginner-to-expert-3-in-1/

Implement solutions from scratch, covering real-world case studies to illustrate the power of neural network models.

上次更新 8/2018

英语 [自动]

3 个章节 • 50 个讲座 • 总时长 5 小时 32 分钟

要求

Basic understanding of Python and R (statistical background plus a basic knowledge) would be useful to anyone taking this course.

A sound knowledge of Anaconda (and its libraries such as NumPy and scikit-learn) is required.

说明

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a wide range of problems in different areas of AI and machine learning. The advantage of neural network is that it is adaptive in nature. It learns from the information provided, i.e. trains itself from the data, which has a known outcome and optimizes its weights for a better prediction in situations with unknown outcome.



R provides this machine learning environment under a strong programming platform, which not only provides the supporting computation paradigm but also offers enormous flexibility on related data processing. The open source version of R and the supporting neural network packages are very easy to install and also simple to learn. Machine learning is widely used in many areas, ranging from the diagnosis of diseases to weather forecasting. You can also experiment with any novel example, which you feel can be interesting to solve using a neural network.



This comprehensive 3-in-1 course is a step-by-step guide to understanding Neural Networks with R; throughout the course, practical, real-world examples help you get acquainted with the various concepts of Neural Networks. Develop a strong background in neural networks with R, to implement them in your applications. Learn 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.



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, Create Your Own Sophisticated Model with Neural Networks, covers one-stop solution to learning complex models with Neural Networks and understanding the basics of Natural Language Processing. With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. Focus on Decision Trees and Ensemble Algorithms. Use scikit-learn to classify text and Multiclass with scikit-learn. Explore various algorithms for classification. Look at Naive Bayes model and Label Propagation. Finally, you'll use Neural Networks using different Classifiers and create your own Simple Estimator.



The third 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. Understand how Neural Networks work and the benefits they offer for supervised and well as unsupervised learning before building our very own neural network. Explore 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.



Taking this course will help you dive head first into the popular field of deep learning as a career choice or for further learning.



By the end of the course, you’ll develop a strong background in neural networks with R, to build and train neural network models and solve complex problems.



About the Authors



● ArunKrishnaswamyhas 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).



● Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab. He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.



● Anshul 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 ecommerce, 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, Southeast 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, Flat 6 labs, 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 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.



此课程面向哪些人：

Anyone who wants to work with neural networks to understand real-world examples. If you are interested in artificial intelligence and deep learning and you want to level up, then this course is what you need!

Programmers, analysts, architects, data scientists, or anyone working in a big data environments and interested in learning how to implement Artificial intelligence in the world of Data Science. Taking this course will help you to take a first step in designing your career as a deep learning architect.