Hands-On Transfer Learning with TensorFlow 2.x
Transfer learning is about using tried-and-tested models to solve similar problems across the domain, with some customization. This book will be your ultimate guide to implementing transfer learning with TensorFlow 2.0.
Starting with the fundamentals of transfer learning, this book will take you through related topics in machine learning and deep learning. You’ll then be able to define your own convolutional neural network models from scratch to solve image classification problems. With the help of sample architectures such as YOLO and RetinaNet, you'll see how object localization works. As you advance, you'll also get to grips with image segmentation architectures such as R-CNN. This book will help you understand the strategy of applying transfer learning in natural language processing (NLP) tasks such as language modeling, sentiment analysis, and language translation. Finally, you'll cover how to apply stochastic gradient descent (SGD) with restart, differential learning rates, and data augmentation techniques to fine-tune models. You’ll not only know the different techniques but also understand when to apply them.
By the end of the book, you'll be able to perform core transfer learning techniques across deep learning domains using TensorFlow 2.x.
|Course Length||9 hours 21 minutes|
|Date Of Publication||13 Nov 2020|