Get yourself trained on Practical Convolutional Neural with this Online Training Practical Convolutional Neural Networks.
Online Training Practical Convolutional Neural Networks
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative e-commerce, and more. 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 is 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.About the AuthorMohit Sewak is an Artificial Intelligence scientist with extensive experience and technical leadership in research, architecture, and solutioning of Artificial Intelligence-driven cognitive and automation products and platforms for industries such as IoT, retail, BFSI, and cyber security.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..Md. Rezaul Karim is a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Center for Data Analytics, Ireland. He was a lead engineer at Samsung Electronics, Korea. He has 9 years’ R&D experience with C++, Java, R, Scala, and Python. He has published research papers on bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, Deeplearning4j, MXNet, and H2O.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.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.