Mohit Sewak - Practical Convolutional Neural Networks: Implement advanced deep learning models using Python
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One stop guide to implementing award-winning, and cutting-edge CNN architectures
Key Features- Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
- Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more
- Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.
This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.
Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision.
By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
What you will learn- From CNN basic building blocks to advanced concepts understand practical areas they can be applied to
- Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it
- Learn different algorithms that can be applied to Object Detection, and Instance Segmentation
- Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy
- Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more
- Understand the working of generative adversarial networks and how it can create new, unseen images
This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
Table of Contents- Deep Neural Networks - Overview
- Introduction to Convolutional Neural Networks
- Build Your First CNN and Performance Optimization
- Popular CNN Models Architectures
- Transfer Learning
- Autoencoders for CNN
- Object Detection with CNN
- Generative Adversarial Network
- Visual Attention Based CNN
Mohit Sewak: author's other books
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First published: February 2018
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Mohit Sewak is a senior cognitive data scientist with IBM, and a PhD scholar in AI and CS at BITS Pilani. He holds several patents and publications in AI, deep learning, and machine learning. He has been the lead data scientist for some very successful global AI/ ML software and industry solutions and was earlier engaged in solutioning and research for the Watson Cognitive Commerce product line. He has 14 years of rich experience in architecting and solutioning with TensorFlow, Torch, Caffe, Theano, Keras, Watson, and more.
Md. Rezaul Karim is a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Center for Data Analytics, Ireland. He was a lead engineer at Samsung Electronics, Korea.
He has 9 years of R&D experience with C++, Java, R, Scala, and Python. He has published research papers on bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, Deeplearning4j, MXNet, and H2O.
Pradeep Pujari is machine learning engineer at Walmart Labs and a distinguished member of ACM. His core domain expertise is in information retrieval, machine learning, and natural language processing. In his free time, he loves exploring AI technologies, reading, and mentoring.
Sumit Pal is a published author with Apress. He has more than 22 years of experience in software, from start-ups to enterprises, and is an independent consultant working with big data, data visualization, and data science. He builds end-to-end data-driven analytic systems.
He has worked for Microsoft (SQLServer), Oracle (OLAP Kernel), and Verizon. He advises clients on their data architectures and build solutions in Spark and Scala. He has spoken at many conferences in North America and Europe and has developed a big data analyst training for Experfy. He has an MS and BS in computer science.
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CNNs are revolutionizing several application domains, such as visual recognition systems, self-driving cars, medical discoveries, innovative e-commerce, and many more. This book gets you started with the building blocks of CNNs, while also guiding you through the best practices for implementing real-life CNN models and solutions. You will learn to create innovative solutions for image and video analytics to solve complex machine learning and computer vision problems.
This book starts with an overview of deep neural networks, with an example of image classification, and walks you through building your first CNN model. You will learn concepts such as transfer learning and autoencoders with CNN that will enable you to build very powerful models, even with limited supervised ( labeled image ) training data.
Later we build upon these learnings to achieve advanced vision-related algorithms and solutions for object detection, instance segmentation, generative (adversarial) networks, image captioning, attention mechanisms, and recurrent attention models for vision.
Besides giving you hands-on experience with the most intriguing vision models and architectures, this book explores cutting-edge and very recent researches in the areas of CNN and computer vision. This enable the user to foresee the future in this field and quick-start their innovation journey using advanced CNN solutions.
By the end of this book, you should be ready to implement advanced, effective, and efficient CNN models in your professional projects or personal initiatives while working on complex images and video datasets.
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