Deep Reinforcement Learning in Machine Learning

In order to get the business value for reinforcement learning, the corporations need to move away from supervised learning with labeled data input/output x/y pairs and unsupervised machine learning, where algorithm discovers the patterns from the data without labels till the results are satisfactory. Reinforcement learning is the most promising machine learning technique that can work on neural networks and deep neural networks and train the agent in a live environment with a goal to find actions than patterns of the data that is needed for gearing towards artificial general intelligence. The session will cover the differences between machine learning and reinforcement learning, history of AI, earlier successes in AI, the state of reinforcement learning adoption in the enterprise in the industries of healthcare, manufacturing, industrial IoT, Robotics, text mining, NLP, NLG, finance, games, retail, and CPG.
Who is this presentation for?
Business and AI integration: Business and Executive level AI practitioners.
Prerequisite knowledge:
Big Data, Analytics Machine Learning
What you'll learn?
In order to get the business value for reinforcement learning, the corporations need to move away from supervised learning with labeled data input/output x/y pairs and unsupervised machine learning, where algorithm discovers the patterns from the data without labels till the results are satisfactory. Reinforcement learning is the most promising machine learning technique that can work on neural networks and deep neural networks and train the agent in a live environment with a goal to find actions than patterns of the data that is needed for gearing towards artificial general intelligence. This is what is needed to power the machines with common sense and consciousness to navigate an environment on earth with LiDAR and no prior knowledge of the environment. It is expected to fuel the future of space exploration where a number of spacecrafts and UAVs that can land on different planets and navigate on their own. It can be implemented for daily consumer with recommendation systems to find the frequently accessed products on eCommerce etc.,

Dr. Ganapathi Pulipaka is a Chief Data Scientist at Accenture for AI strategy, architecture, application development of Machine learning, Deep Learning algorithms with experience in deep learning reinforcement learning algorithms, IoT platforms, Python, R, and TensorFlow, Big Data, IaaS, IoT, Data Science, Blockchain, Apache Hadoop, Apache Kafka, Apache Spark, Apache Storm, Apache Flink, SQL, NoSQL, Mathematics, Data Mining, Statistical Framework, SIEM with SAP Cloud Platform Integration, AWS, Azure, GCP with 9+ Years of AI Research and Development Experience and 20+ years of experience as SAP Technical Development and Integration Lead with 30 project implementations for Fortune 100 companies. He is a PostDoc Research Scholar in Machine Learning, Big Data Analytics, Robotics, and Data Science as part of Doctor of Computer Science Program from Colorado Technical University, Colorado Springs. He also holds another PhD in Business Administration, Data Analytics, and Enterprise Resource Management from California University, Irvine. He is also a bestselling author of multiple books on big data analytics, machine learning, robotics, and data science and leading contributor of machine learning articles for various publications.