Convolutional neural network neural networks .pptx

Convolutional neural network neural networks .pptx

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  Convolutional Neural Network Presentedby: Reference:What Is a Convolutional Neural Network? A Beginner's Tutorial for Machine Learning and Deep Learning (freecodecamp.org)
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  Convolutional Neural Network(CNN) ❏ A convolutional neural network is a specific kind of neural network with multiple layers. It processes data that has a grid-like arrangement then extracts important features. ❏ CNNs use convolutions ,which is used instead of matrix multiplication in at least one layer of the CNN. ❏ The more data there is available, the better tuned the CNN will be.
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  How CNN works? ❏Convolutional neural networks are made of layers of artificial neurons called nodes. ❏ These nodes are functions that calculate the weighted sum of the inputs and return an activation map. ❏ Each node in a layer is defined by its weight values. ❏ When you give a layer some data, like an image, it takes the pixel values and picks out some of the visual features. ❏ Usually with images, a CNN will initially find the edges of the picture. ❏ Then this slight definition of the image will get passed to the next layer. ❏ Then that image definition will get passed to the next layer and the cycle continues until a prediction is made.
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  How CNN works? ❏As the layers get more defined, this is called max pooling. ❏ It only returns the most relevant features from the layer in the activation map. ❏ This is what gets passed to each successive layer until you get the final layer. ❏ The last layer of a CNN is the classification layer which determines the predicted value based on the activation map.
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  Types of CNN ❏1D CNN: With these, the CNN kernel moves in one direction. 1D CNNs are usually used on time-series data. ❏ 2D CNN: These kinds of CNN kernels move in two directions. You'll see these used with image labelling and processing. ❏ 3D CNN: This kind of CNN has a kernel that moves in three directions. With this type of CNN, researchers use them on 3D images like CT scans and MRIs.
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  Applications of CNN ❏Recognize images with little preprocessing ❏ Recognize different hand-writing ❏ Computer vision applications ❏ Used in banking to read digits on checks ❏ Used in postal services to read zip codes on an envelope
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  Conclusion  Multi-layer networksadept at extracting features from data, particularly effective with images.  Minimal pre-processing requirement, making them suitable for various applications.  Utilizes convolutions and pooling to distill images to essential features, enhancing accurate identification.  Trains effectively with fewer initial parameters compared to other neural networks.  Reduced need for extensive hidden layers due to convolutional capabilities in feature discovery.
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  THANKS!