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Convolution neural networks can also help to classify colored images There are two challenges when dealing with colored images Colored images are of different sizes. Images of different colors Colored images have different sizes and colors, unlike the Fashion MNIST dataset with images of the same size 28X28 with one color(Greyscale). How are we handling different sizes of images ..? The neural network needs a fixed-size input, so we will resize all colored images to fixed-size. Here we are converting images to 150X150 and flattened for neural network input. How are we handling the different colors of the image ..? If a greyscale image is 6X6 then it is converted into a matrix of size 6X6 but in the colored image, we will have three dimensions. Color images are represented by three color channels - Red,Blue, Green. All three colored channels are combined to form a colored image and each of these colored channels is represented by a 2-dimensional array. So the depth of the image is a sta...

  Objective : Develop a neural network model capable of recognizing clothing items using the Fashion MNIST dataset. Fashion MNIST dataset :  has 28X28 pixel size images, it contains 70,000 images of shirts, pants, boots, etc. Training and Test data : Fashion MNIST Dataset Input : An image of Fashion Output : Able to tell the item with probability. Code link : colab code link - Fashion MNIST The solution we got from Deep neural network solution is as below  Now we solve the see the same Fashion MNSIT classification problem using CNN (Convolution neural network). Convolution Neural networks provide better accuracy than Deep neural networks. Let's understand the Convolution Neural network.  There are two main concepts of CNN Convolutions  Max Pooling Convolutions :  The idea of convolution is to create another grid of numbers called kernel or filter which will scan over original pixel values to generate convoluted image. For Corner values,  'o' values wil...

Objective: The goal is to develop a machine-learning model capable of converting Celsius to Fahrenheit. This model will be trained using a dataset containing pairs of Celsius and Fahrenheit values. The conversion will be based on the formula   F=C×1.8+32 . After training, the model’s performance will be evaluated by comparing its predictions with the results obtained from the formula. Below are the important steps for building a Neural network model Import dependencies   - Tensorflow , numpy , etc Setup Training data -  Features - Input Labels - output Examples - Input/output pairs for model training. Create a neural network model Build a layer Assemble layers into the model Compile the model with loss and optimization functions Train the model -  The training model is about tuning the internal variables of the networks to the best possible values so that they can map the input to the output Use the model to predict the weights Example Code which you can paste...