Deep Learning: CNN model from basic and its implementation in Keras for Image Classification

Medium Link: / deep-learning-cnn-mode...
1. What is CNN ?
Computer vision is evolving rapidly day-by-day. Its one of the reason is deep learning. When we talk about computer vision, a term convolutional neural network( abbreviated as CNN) comes in our mind because CNN is heavily used here. Examples of CNN in computer vision are face recognition, image classification etc. It is similar to the basic neural network. CNN also have learnable parameter like neural network i.e, weights, biases etc.
2. Why should we use CNN ?
Problem with Feedforward Neural Network
Suppose you are working with MNIST dataset, you know each image in MNIST is 28 x 28 x 1(black & white image contains only 1 channel). Total number of neurons in input layer will 28 x 28 = 784, this can be manageable. What if the size of image is 1000 x 1000 which means you need 10⁶ neurons in input layer. Oh! This seems a huge number of neurons are required for operation. It is computationally ineffective right. So here comes Convolutional Neural Network or CNN. In simple word what CNN does is, it extract the feature of image and convert it into lower dimension without loosing its characteristics. In the following example you can see that initial the size of the image is 224 x 224 x 3. If you proceed without convolution then you need 224 x 224 x 3 = 100, 352 numbers of neurons in input layer but after applying convolution you input tensor dimension is reduced to 1 x 1 x 1000. It means you only need 1000 neurons in first layer of feedforward neural network.
3.1 Image Representation
Thinking about images, its easy to understand that it has a height and width, so it would make sense to represent the information contained in it with a two dimensional structure (a matrix) until you remember that images have colors, and to add information about the colors, we need another dimension, and that is when Tensors become particularly helpful.
Images are encoded into color channels, the image data is represented into each color intensity in a color channel at a given point, the most common one being RGB, which means Red, Blue and Green. The information contained into an image is the intensity of each channel color into the width and height of the image, just like this

[@rajanihiwrale]

Hello sir ..please help mi with it jst tell mi what will be the procress which data should i use for this

[@rajanihiwrale]

Helo sir ..actually i need some sujjetion from u ..my thesis topic is crop production and yeild prediction using machine learning so im using algorithms like gradient boosting and Random Forest as well as using a satellite images sentinel 2 for it so what will be the procress for this what will be the procress please can u tell mi please sir ..actully i wanted to submit in 2 months this all theis report please can u guid mi