- Describe the concept of transfer learning.
- When can the transfer learning process bring good results?
- What are the differences between transfer learning and fine-tuning?
- If a model has been trained on a small dataset with low variance (similar examples), is it an excellent candidate to be used as a fixed-feature extractor for transfer learning?
- The flower classifier built in the transfer learning section has no performance evaluation on the test dataset: add it.
- Extend the flower classifier source code, making it log the metrics on TensorBoard. Use the summary writers that are already defined.
- Extend the flower classifier to save the training status using a checkpoint (and its checkpoint manager).
- Create a second checkpoint for the model that reached the highest validation accuracy.
- Since the model suffers from overfitting, a good test is to reduce the number of neurons...
Hands-On Neural Networks with TensorFlow 2.0
By :
Hands-On Neural Networks with TensorFlow 2.0
By:
Overview of this book
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.
This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.
By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
Preface
What is Machine Learning?
Neural Networks and Deep Learning
Section 2: TensorFlow Fundamentals
TensorFlow Graph Architecture
TensorFlow 2.0 Architecture
Efficient Data Input Pipelines and Estimator API
Section 3: The Application of Neural Networks
Image Classification Using TensorFlow Hub
Introduction to Object Detection
Semantic Segmentation and Custom Dataset Builder
Generative Adversarial Networks
Bringing a Model to Production
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