Convolutional neural network neural networks .pptx

Its about convolutional neural networks

1. Convolutional Neural Network Presented by: Reference: What Is a Convolutional Neural Network? A Beginner's Tutorial for Machine Learning and Deep Learning ( freecodecamp.org )

2. 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.

3. 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.

4. 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.

5. 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.

6. 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

7. Conclusion Multi-layer networks adept 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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