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Who should attend
This program addresses the needs of developers, technologists, researchers, and engineers across a wide range of technology-driven industries. Participants should have a working knowledge of Python or R. You will examine TensorFlow and ML approaches for applications in business intelligence and analytics, data mining, predictive maintenance, robotics behavior design, product development, marketing, operations improvement. Participants are prepared for advanced domains such as autonomy, internet of things, edge computing, cognitive computing/AI, computer vision, and natural language processing.
About the course
Learn the fundamental concepts of neural networks and deep learning. One of the hottest fields in artificial intelligence, deep learning is the key to solving highly complex problems such as training autonomous vehicles or object recognition. New applications are being found every day across manufacturing, pharmaceutical, medical, security, transportation, and aerospace.
In this course, you will have the chance for hands-on exploration of TensorFlow, Google’s recently released in-house toolset for deep learning. It is a Python-based library that runs on graphics processing units and tensor processing units, executing trillions of instructions per second in parallel. A solid understanding of TensorFlow is critical for anyone working in fields involving AI and machine learning.
You will examine TensorFlow, GPU and TPU architectures, and machine learning approaches for applications in business intelligence and analytics, data mining, predictive maintenance, robotics behavior design, product development, marketing, operations improvement. When you complete the course, you will be prepared for advanced domains such as autonomy, internet of things, edge computing, cognitive computing/AI, computer vision, and natural language processing.
You will learn how to:
- Install TensorFlow software and access it via Python and R
- Understand the all Machine Learning (ML) Models
- Solve linear algebra related math problems in TensorFlow
- Understand Neural Network architecture and Deep Neural Networks
- Build Neural Networks models in TensorFlow
- Understand optimization algorithms - Gradient Descent + Adam – in TensorFlow
- Implement Backpropagation algorithm in TensorFlow
- Simulate Linear Regression, kNN, Clustering in TensorFlow
- Implement Convolution Neural Networks (CNN) in TensorFlow
- Implement Recurrent Neural Network (RNN) in TensorFlow
- Understand Reinforcement Learning
- Understand the role of TensorBoard in visualization
- Machine Learning + Neural Networks + Deep Learning
- Tools for Building Deep Learning
- TensorFlow Architecture
- Writing TensorFlow Programs
- Building Neural Networks in TensorFlow – Categorical and Numerical output
- Optimization - Gradient Descent, Adam, backpropagation
- Linear regression
- Convolution Neural Networks - Image classification
- Recurrent Neural Networks - Sequence to Sequence
- Reinforcement Learning
Ash Pahwa, Ph.D., is an educator, author, entrepreneur, and technology visionary with three decades of industry and academic experience. He has founded several successful technology companies during his career, the latest of which is A+ Web Services. His expertise includes search engine optimizat...
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