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9 Free Resources To Learn Transfer Learning

9 Free Resources To Learn Transfer Learning

Ambika Choudhury

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Transfer learning can be said as a shortcut to solving complex machine learning problems. In simple words, this learning is used to enhance the learning of the model, shorten the time as well as make the learning process quick for the current task. This technique can be applied in computer vision when the model has to learn from images or videos and in NLP techniques.

In this article, we list down the top 9 free resources in Transfer Learning one must-read.



(The list is in no particular order)

1| Transfer Learning With a Pre-trained ConvNet

About: This tutorial is provided by the developers of TensorFlow, where you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network. You will learn hands-on exercise on data pre-processing, feature extraction, fine-tuning, and other such.

Click here to know more.

2| Practical Transfer Learning (Deep Learning) in Python

About: In this course, you will learn how to implement Transfer Learning in image classification. The topics include pre-trained model, fine-tuning, feature extraction techniques and other such. You will gain a basic understanding of Python, machine learning, including convolutional neural networks.

Click here to know more.

3| Transfer Learning with Keras and Deep Learning

About: In this tutorial, you will learn how to perform transfer learning with Keras, deep learning, and Python on your own custom datasets. You will learn how to create an automated computer vision application that can distinguish between “food” and “not food” using transfer learning.

Click here to know more.

4| Transfer Learning for Computer Vision 

About: In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You will learn how to train a model to classify ants and bees and will cover two major transfer learning scenarios: fine-tuning the convnet and convnet as a fixed feature extractor.

Click here to know more.

5| Transfer Learning – Machine Learning’s Next Frontier

About: In this tutorial, you will learn an overview of transfer learning. The topic includes here are applications of transfer learning, transfer learning models, why transfer learning needs attention, its scenarios, and other related research areas such as how to improve model’s ability to generalise, multi-task learning, continuous learning, zero-shot learning and other such. 

Click here to know more.

6| What is Transfer Learning? Exploring the Popular Deep Learning Approach 

About: In this tutorial, you will learn what transfer learning is, how it works, why and when it should be used. Additionally, you will cover the different approaches of transfer learning, including some resources on already pre-trained models, feature extraction, how to train a model to reuse it, among others.

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7| A Survey of Transfer Learning 

About: This survey paper will help you understand transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. You will also gain knowledge on software downloads for various transfer learning solutions and a discussion of possible future research work.  

Click here to know more.

8| Deep Learning, Transfer Learning and Turtles

About: In this tutorial, you will learn how to use transfer learning and Meeshkan to teach a machine to tell the difference between Teenage Mutant Ninja Turtles and Koopa Troopas. You will also learn how to apply the turtle-detector to real turtle images in order to classify if the real turtles look more like TMNTs or Koopa Troopas. 

Click here to know more.

9| Keras Tutorial: Transfer Learning Using Pre-Trained Models

About: In this tutorial, you will learn about transfer learning and how to train a new model for a different classification task. The topics include transfer learning vs fine-tuning, training a network in Keras, applications of transfer learning, among others.

Click here to know more.

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