Book Image

The Deep Learning Workshop

By : Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
Book Image

The Deep Learning Workshop

By: Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)
Preface

Fine-Tuning

In the previous section, we learned how to apply transfer learning and use pretrained models to make predictions on our own dataset. With this approach, we froze the entire network and trained only the last few layers that were responsible for making the predictions. The convolutional layers stay the same, so all the filters are set in advance and you are just reusing them.

But if the dataset you are using is very different from ImageNet, these pretrained filters may not be relevant. In this case, even using transfer learning will not help your model accurately predict the right outcomes. There is a solution for this, which is to only freeze a portion of the network and train the rest of the model rather than just the top layers, just like we do with transfer learning.

In the early layers of the networks, the filters tend to be quite generic. For instance, you may find filters that detect horizontal or vertical lines at that stage. The filters closer to the end of...