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Tensor Flow Machine Learning Transfer Learning

Learn about the fundamentals of Convolutional Neural Network and transfer learning in this free online course.

Publisher: NPTEL
This free online course in Tensor Flow Machine Learning transfer learning will introduce you to a new neural network architecture known as Convolutional Neural Network (CNNs). You will also learn about image classification and visualization as well as transfer Learning with pre-trained Convolutional Neural Network and TensorFlow hub. You will also be introduced to the method of using Estimator API to create machine learning models.
Tensor Flow Machine Learning Transfer Learning
  • Duration

    4-5 Hours
  • Students

    510
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

This free online course in Tensor Flow Machine Learning to transfer learning will begin by introducing you to the concept of Convolutional Neural Networks, otherwise known as CNNs. You will also learn about convolution operations, the procedure for the creation of filters as well as the evaluation of filters. This course will introduce you to the key operations in Convolution Neural Networks and demonstrate their usage in recognizing handwritten digit task. You will also learn about pooling, model summary and the differences between CNNs and feed-forward neural network.

This course then introduces the importance of pre-trained machine learning model as a feature extractor. You will also learn the procedure for fine-tuning a pre-trained model. Next, you will be introduced to the process of building image classification models from scratch using strategies employed in practice. You will also learn how to visualize what the Convolution Neural Network is learning.

The course then explains the process of using Tensorflow Keras as an API to build machine learning models. You will also learn about the overview of programming to estimators, input functions and feature columns. This course explains in great detail the importance of the boosted tree model and how to utilize Tensorflow estimator API to train boosted tree classifiers. You will learn how to create input functions and feature columns. You will also learn about the importance of embedding projectors, encoding, embedding layers and decoding

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