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Transfer Learning With TensorFlow — Re-using Po...

Transfer Learning With TensorFlow — Re-using Powerful Models

Event Name: TensorFlow Dev Summit Nairobi

Topic: Transfer Learning With TensorFlow — Re-using Powerful Models // TFDevSummit Nairobi

Olayinka Peter Oluwafemi

June 24, 2020
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  1. Why TensorFlow? Apart from the excellent functionalities, services and high-level

    ops for advanced computations, TensorFlow was built to be production ready — making scalability an easy-go!
  2. Production-ready A great example is how easy it is to

    re-use powerful models in TensorFlow. This technique in machine learning is called Transfer Learning.
  3. Transfer learning and domain adaptation refer to the situation where

    what has been learned in one setting (i.e., distribution P1) is exploited to improve generalization in another setting (say distribution P2). — Extracted from Deep Learning, pg 536 “
  4. Transfer Learning is a technique where a model which has

    already been trained on one problem is used in some way on a second related problem. The advantages are numerous, inclusive of: • Learning a new task relies on previously learned tasks • Less training data is needed, hence learning process is faster • Learning gets to be more accurate
  5. Transfer Learning for Image Classification Our codelab will cover Transfer

    Learning for Image Classification using several pre-trained powerful deep learning models built by researchers in the past. We’ll use the following pre-trained models: • MobileNet • ResNet • Inception (aka GoogleNet)
  6. Meet TensorFlow Hub Quite interestingly, TensorFlow 2.x comes with a

    lot of these pre-trained models to be easily plugged in to your code and utilized. Amazing right? Meet tensorflow.org/hub