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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).

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