Tensorflow 2.0: Building computer vision applications
- Offered byGreat Learning
Tensorflow 2.0: Building computer vision applications at Great Learning Overview
Tensorflow 2.0: Building computer vision applications
at Great Learning
Duration | 2 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course |
Credential | Certificate |
Tensorflow 2.0: Building computer vision applications at Great Learning Highlights
Tensorflow 2.0: Building computer vision applications
at Great Learning
- Earn a certificate of completion from Great Learning
Tensorflow 2.0: Building computer vision applications at Great Learning Course details
Tensorflow 2.0: Building computer vision applications
at Great Learning
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
Tensorflow 2.0: Building computer vision applications
at Great Learning
Syllabus
Array
Tensorflow 2.0: Building computer vision applications at Great Learning Faculty details
Tensorflow 2.0: Building computer vision applications
at Great Learning
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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