Hands-On Transfer Learning with TensorFlow 2.0: Transfer experience from models using TensorFlow 2.0

Transfer learning involves using a pre-trained model on a new problem. It is currently very popular in the field of Deep Learning because it enables you to train Deep Neural Networks with comparatively little data. In Transfer learning, knowledge of an already trained Machine Learning model is applied to a different but related problem. The general idea is to use knowledge, which a model has learned from a task where a lot of labeled training data is available, in a new task where we don't have a lot of data. Instead of starting the learning process from scratch, you start from patterns that have been learned by solving a related task. In this course, learn how to implement transfer learning to solve a different set of machine learning problems by reusing pre-trained models to train other models. Hands-on examples with transfer learning will get you started, and allow you to master how and why it is extensively used in different deep learning domains. You will implement practical use cases of transfer learning in CNN and RNN such as using image classifiers, text classification, sentimental analysis, and much more. You'll be shown how to train models and how a pre-trained model is used to train similar untrained models in order to apply the transfer learning process even further. Allowing you to implement advanced use cases and learn how transfer learning is gaining momentum when it comes to solving real-world problems in deep learning. By the end of this course, you will not only be able to build machine learning models, but have mastered transferring with tf.keras, TensorFlow Hub, and TensorFlow Lite tools. The code bundle for this course is available at https://github.com/PacktPublishing/Hands-On-Transfer-Learning-with-TensorFlow-2.0-Video

If you want to take deep learning to the next level and master transfer-learning concepts, then this course is what you need. Requirement: Basic knowledge of machine learning and Python is expected to get the most out of the video.