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TF03 : Go further on Convolutional Neural Networks

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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...
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Android Automotive Course (CH01): Android Automotive Overview

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 Android Automotive Android Automotive is a base Android platform specifically designed for use in vehicle dashboards. It provides a full-stack solution, including both the underlying operating system and the user interface layer. Android Automotive is highly customizable, allowing implementers (such as car manufacturers) to differentiate their products according to their vision and requirements. Difference between Android Auto and Android Automotive Android Auto : This platform runs on the user's phone and projects the Android Auto user experience to a compatible in-vehicle infotainment system via USB. It supports apps designed for in-vehicle use. Android Automotive : It operates directly on the in-vehicle hardware, providing a complete infotainment platform. Android Automotive supports both Android apps and those built specifically for Android Auto. Android Open Source Project AOSP is the open-source operating system project maintained by Google and AOSP is the foundation upon w...
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TF01 : Understanding Neural Networks using TensorFlow

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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...
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