Deep Learning With TensorFlow
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Deep Learning--one of the hottest fields in Artificial Intelligence (AI)--uses neural network architectures to solve highly complex learning problems like training autonomous vehicles or object recognition. New applications are being found every day across manufacturing, pharmaceutical, medical, security, transportation, and aerospace. Other facets of AI, like computer vision, mixed reality, natural-language processing are driving extensibility and possibility for machine learning.
TensorFlow, Google's recently released in-house toolset, is a leading solution in the AI/ML space. It is a Python-based library that runs on GPU (Graphics Processing Units) and TPU (Tensor Processing Units) processors, developed primarily for AI applications. Executing trillions of instructions per second in parallel, it is well-suited for solving Deep Learning problems. A solid understanding of TensorFlow is critical to anyone working in fields involving AI/ML.
Learn the fundamental concepts of neural networks and deep learning. Examine TensorFlow hands-on through Python as we investigate Machine Learning modeling methods for estimation and classification, as well as explore GPU and TPU architectures.
Participants 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
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.