Objectives: 

• Understand cloud-simulation technology 

• Setting up a mesh convergence study which is an essential part of the FEM

• Concept in the Finite Element Method, namely mesh independence or mesh convergence.

• Use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision

• Observe how tuning TF-TRT parameters affects performance and inference throughput

• Tensorflow, Python Programming, Deep Learning, keras, NVIDIA TensorRT (TF-TRT)

• Understand cloud-simulation technology

• Setting up a CFD pump simulation

• Simulations for Computational Fluid Dynamics 

• Create a Route Analysis detailing How to implement structured change initiatives using a sample business case

• Change Management and Business Process Reengineering

• Leadership, Management, Strategic Initiatives and Program Development. 

• Nonlinear FEM, Material Science, Finite Element Analysis and Finite Element Method

• Simulation

• Quantify risk-to-reward using Treynor Ratio

• Calculate value at risk for investment portfolio, Financial Data Analysis, Risk Management, Statistics

• Construct regex patterns

• Extract patterns and replace strings with regex

• Python Programming & Regular Expression (REGEX)

• Implement gradient ascent algorithm

• Visualize image features that maximally activate filters of a CNN

• Tensorflow, Convolutional Neural Network and Artificial Neural Network

• Machine Learning and Deep Learning

• Setting incompressible fluid flow simulation around a spoiler and post-process the result

• Cloud Based Simulation

• Computational Fluid Dynamics (CFD), Fluid Mechanics

• Analyze business processes and find solutions to existing business problems

• Define your business processes, their objectives and how they flow within the organizational context

• Use a Tokenizer in TensorFlow

• Pad and Truncate Sequences

• Create and Train a Recurrent Neural Network

• Use NLP and Deep Learning to perform Text Classification