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Tensorflow 2.0: Building computer vision applications 

  • Offered byGreat Learning

Tensorflow 2.0: Building computer vision applications
 at 
Great Learning 
Overview

Duration

2 hours

Total fee

Free

Mode of learning

Online

Difficulty level

Beginner

Official Website

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Credential

Certificate

Tensorflow 2.0: Building computer vision applications
 at 
Great Learning 
Highlights

  • Earn a certificate of completion from Great Learning
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Tensorflow 2.0: Building computer vision applications
 at 
Great Learning 
Course details

More about this course
  • Tensorflow is one of the most popular deep learning libraries and deep learning has been responsible for some of the interesting applications such as: COVID19 detection using chest x-rays, Language translation, Self-driving cars
  • In this lecture, we focus on building COVID19 detection solutions using Tensorflow 2.0 and Keras
  • Relevant Jupyter notebooks will be provided for the participants to practice the ideas covered in the lecture

Tensorflow 2.0: Building computer vision applications
 at 
Great Learning 
Curriculum

Syllabus

Array

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Tensorflow 2.0: Building computer vision applications
 at 
Great Learning 
Faculty details

Dr. D Narayana
Designation : Professor, Artificial Intelligence and Machine Learning, Great Learning Description : Dr. Narayana holds a PHD in Mathematics from Pierre and Marie Curie University, Paris, France. Dr. Narayana has more than 11 years of industry experience and over 4 years academic research experience. Over these years, he has worked as a professor, trader, project manager, team lead, and developer. His area of expertise includes Machine learning, Optimization, Financial engineering and High frequency & algorithmic trading. He is proficient in R Language, Hadoop, Python & Big Data Technologies. He has published over ten research articles in international journals and conferences. He has taught courses in Big Data technologies, guided student projects and mentored students at premier institutes.
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Tensorflow 2.0: Building computer vision applications
 at 
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