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CNN – Padding
CNN – Padding Syllabus: What Is Padding In CNN? Why We Use Padding? Types Of Padding. Examples Of Padding. Key Points. (1) What Is Padding In CNN? Padding in Convolutional Neural Networks (CNNs) refers to adding extra pixels (usually zeros) around the edges of an input image or feature map. The purpose of padding is to control the spatial dimensions of the output feature map after applying a convolution operation. (2) Why We Use Padding? (3) Types Of Padding. (4) How Much Padding We Should Do Such That The Input & Output Size Will Be Same. The Formula for Output
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CNN – What Is Convolution Operation?
Convolution Operation Syllabus: Purpose of Convolution How Convolution Works Convolution Layers Filter Basics Weight Initialization Learned Filters Multiple Filters (1) Purpose Of Convolution The purpose of convolution in the context of image processing, computer vision, and convolutional neural networks (CNNs) is to extract and process features from input data efficiently. It enables the model to understand patterns, spatial hierarchies, and key attributes in the data, such as edges, textures, shapes, and objects. (2) How Convolution Works? Convolution is a fundamental operation in Convolutional Neural Networks (CNNs), used primarily for feature extraction. It allows the network to automatically learn spatial hierarchies
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CNN – Key Concepts
Key Concepts Syllabus: What Is An Edge Detection Why Edge Detection Is Important? Difference Between Black & White And Color Images. What Is Spatial Information And How It Is Preserved In The Feature Map? (1) What Is An Edge Detection? Edge detection is a technique used in image processing and computer vision to identify the boundaries or edges within an image. Edges are regions in an image where there is a significant change in intensity or color, typically indicating the boundaries of objects or features in the image. (2) Why Is Edge Detection Important? Edges represent important structural information about
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CNN – What Is CNN ?
What Is An CNN? Syllabus: What Is CNN? Differences between CNNs and fully connected networks. Applications of CNNs. (1) What Is CNN? A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed to process and analyze grid-like data structures, such as images. It is widely used for tasks such as image recognition, object detection, and video analysis, among others. (2) Difference Between CNN and ANN? The main difference between Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) lies in their structure and the types of tasks they excel at. Here’s a simple breakdown: (3) What
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CNN – Convolutional Neural Networks.
Convolutional Neural Networks Syllabus: 1. Fundamentals of CNNs What are CNNs? Differences between CNNs and fully connected networks. Applications of CNNs. Components of CNNs Convolution layers. Filters (Kernels) and their role. Stride, Padding, and their effects. Activation Functions ReLU, Sigmoid, Tanh, etc. Pooling Layers Max pooling, Average pooling, Global pooling. Fully Connected Layers How they integrate features learned from convolutional layers. 2. Advanced Architectures and Concepts Popular CNN Architectures LeNet, AlexNet, VGG, ResNet, Inception, MobileNet, EfficientNet. Residual Networks (ResNets) Skip connections and their significance. Inception Networks Factorized convolutions and mixed architecture. Depthwise Separable Convolutions Efficiency in MobileNet and EfficientNet. Dilated
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For A Batch Of 100 Records How The Records Will Be Passed At A Time Or One By One?
For A Batch Of 100 Records How The Records Will Be Passed At A Time Or One By One? Answer: For a batch of 100 records in a neural network, all 100 records are passed through the network at the same time (in parallel), not one by one. This is made possible through vectorized operations and the efficient handling of data by modern hardware like GPUs. Why Are Records Processed Simultaneously? How It Works Internally: Summary: How Individual Neurons Operation Happens: Yes, the operations we discussed (matrix multiplication, bias addition, and activation) apply to individual neurons as part of the
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When Does The Weights Of The Neural Networks Are Updated?
When Does The Neural Network Is Updated? Answer: The weights in a neural network are typically updated after each batch during training, not after each epoch. This depends on the type of gradient descent being used. Types of Gradient Descent and Weight Updates:
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What Is An Epoch ?
What Is An Epoch? (1) What Is An Epoch? An epoch in deep learning refers to one complete pass through the entire training dataset by the neural network during training. It is a key concept when training machine learning models, especially neural networks, to understand how data is processed and how the model learns. (2) Key Points About Epochs: (3) Examples Of Epochs (4) Why Are Multiple Epochs Needed? (5) Choosing the Right Number of Epochs (6) Visualization
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What Is Hierarchical Representations In Deep Learning?
What Is Hierarchical Representations In Deep Learning? Table Of Contents: What Is Hierarchical Feature Representation? Key Concepts of Hierarchical Representations. (1) What Is Hierarchical Feature Representation? Hierarchical representations refer to the layered structure of features or patterns that a machine learning model, particularly in deep learning, learns from input data. These representations progress from simple, low-level features in early layers to more complex, high-level abstractions in deeper layers of a neural network. (2) Key Concepts In Hierarchical Representation. (3) Benefits Of Hierarchical Representation.
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Probability Theory
Probability Theory Table Of Contents: Probability Of ‘A’ and ‘B’ Happening Together. Probability Of ‘A’ Given ‘B’ has already Happened. (1) Probability Of ‘A’ and ‘B’ Happening Together. P(A∩B)=P(A)×P(B) for an independent event if there is no relationship between a and b how we are multiplying there individual probability As here we are considering two events we need to consider all possible outcomes for both the events. For rolling two dies together we will have 36 number of outcomes. For tossing two coins together we will hae 4 possible outcomes. (2) Probability Of ‘A’ Given ‘B’ Has Already Happened. (3)
