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The TensorFlow Workshop

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So , Maddalone
4.6 (25)
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The TensorFlow Workshop

The TensorFlow Workshop

4.6 (25)
By: Matthew Moocarme, Abhranshu Bagchi, Anthony So , Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
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Preface

ImageNet

ImageNet is a large dataset containing more than 14 million images annotated for image classification or object detection. It was first consolidated by Fei-Fei Li and her team in 2007. The goal was to build a dataset that computer vision researchers could benefit from.

The dataset was presented for the first time in 2009, and every year since 2010, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been organized for image classification and object detection tasks.

Figure 8.1: Examples of images from ImageNet

Over the years, some of the most famous CNN architectures (such as AlexNet, Inception, VGG, and ResNet) have achieved amazing results in this ILSVRC competition. In the following graph, you can see how some of the most famous CNN architectures performed in this competition. In less than 10 years, performance increased from 50% accuracy to almost 90%.

Figure 8.2: Model benchmarking...

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