Deep Dive into Different Types of Convolutions for Deep Learning
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Deep Dive into Different Types of Convolutions for Deep Learning

About the Book

Deep Dive into Different Types of Convolutions for Deep Learning introduces wide range of convolution operators in the field of Deep Learning. As a deep learning researcher/engineer, in many real-world projects you will need to design special deep models that will require extensive knowledge of how to represent different data types and other components of deep networks (such as different types of convolution operators). I believe that there is a shortage of resources in the field of deep learning, according to the viewpoint that most of them have expressed their content in a very superficial way, and without describing the necessary details. Accordingly, since the convolution operator plays a critical role in the design of various types of Convolutional Neural Networks (CNN models), this book provides a detailed description of the various types of convolution operators with other necessary supplementary materials. Here are some of the concepts you will learn when reading this book:

  • You will learn how to represent and model different kinds of data (e.g., time series data, textual data, image data, video data, audio data, medical data (Genomics-based and Radiomics-based)) for deep learning projects
  • You will learn the fundamental concepts of convolution operator (such as its relation to cross-correlation, how to apply it on different kinds of data)
  • You will be thoroughly familiar with how the convolution operator is represented, and its related arithmetic (i.e., a general representation format for different types of convolutions is presented which is compatible with PyTorch, Keras, and TensorFlow frameworks). Also all of the points about the activation map shapes (output tensors shapes), and their number of learnable parameters are also mentioned
  • You will be well acquainted with the concept of 3D convolution and its applications (e.g., in video understanding tasks (such as video activity recognition, designing 3D ConvNets, etc.), and 3D data processing)
  • You will be well acquainted with the concept of 1D convolution operator, and its applications in textual data processing, natural language processing (NLP) tasks, and data science projects
  • You will be familiar with the 1 × 1 convolution operator, and its critical role in Network Distillation topic, Inception module of GoogLeNet, etc.
  • You will learn unique notes about Transposed convolution or Deconvolution, how to apply it in various tasks (e.g., semantic image segmentation, object detection, etc.), its arithmetic notes, and so on
  • You will be well acquainted with the concepts of Dilated convolution, its different types, and its arithmetic notes
  • You will completely learn necessary notes about the Receptive Field calculations
  • You will thoroughly learn extremely useful notes and techniques about the different methods of Separable convolution (i.e., Spatially Separable convolution, Depthwise Separable convolution, and Pseudo-3D Convolution)
  • You will learn how to apply Grouped convolution in general cases (i.e., on 2D and 3D data types)
  • You will get lots of interesting and useful ideas on advanced cutting edge convolution techniques, such as: Deformable convolution, Shuffled Grouped convolution, 3D Temporal Deformable convolution, etc.

In fact, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and learn how to tackle with practical tasks in computer vision, natural-language processing, audio processing, etc. by using different kinds of convolutions. Reading this book is recommended to all researchers, and engineers in this field with any level of knowledge (especially it would be a great handbook for deep learning researchers to design state-of-the-art CNN models). By the time you finish, you'll have the great knowledge and hands-on skills to apply different convolution operators in your own projects (i.e., for designing special CNN models).

About the Author

Amir Hossein Karami
Amir Hossein Karami

I am a Senior Deep Learning Research Scientist. I am experienced in Machine Learning, Deep Learning, and Computer Vision. I have done a lot of projects in these domains for the industry. You can follow me on:

(my LinkedIn Profile)

(my GitHub Profile)

(My Google Scholar Profile)

Table of Contents

  • Preface
  • Chapter 1: Data Representation
    • 1.1 Scalar (0D tensor)
    • 1.2 Vector (1D tensor)
    • 1.3 Matrix (2D tensor)
    • 1.4 3D tensor and tensor of higher dimensions
    • 1.5 The concept of data batches
    • 1.6 Real-world examples of data tensors
    • 1.7 Vector data
    • 1.8 Time series data or sequence data
    • 1.9 Image data
    • 1.10 Video data
    • 1.11 Audio data
  • Chapter 2: Convolution and Cross-Correlation
  • Chapter 3: Convolution on One-dimensional Images
  • Chapter 4: Convolution on Multi-dimensional Data
  • Chapter 5: 2D Convolution Arithmetic
  • Chapter 6: 3D Convolution
  • Chapter 7: 3D Convolution Arithmetic
  • Chapter 8: 1D Convolution
  • Chapter 9: 1D Convolution Arithmetic
  • Chapter 10: 1 × 1 Convolution
  • Chapter 11: Transposed Convolution (Deconvolution)
    • 11.1 Transposed convolution arithmetic
    • 11.2 Checkerboard artifacts
  • Chapter 12: Dilated Convolution
    • 12.1 Gridding artifacts
    • 12.2 Dilated convolution arithmetic
  • Chapter 13: Receptive Field
  • Chapter 14: Separable Convolution
    • 14.1 Spatially separable convolution
    • 14.2 Depthwise separable convolution
    • 14.3 Pseudo-3D convolution
  • Chapter 15: Grouped Convolution
    • 15.1 Shuffled grouped convolution
    • 15.2 Pointwise grouped convolution
  • Chapter 16: Deformable Convolution
  • Chapter 17: Representation Summary
  • Notes

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