Image classification using convolutional neural networks (CNNs) is fairly easy today, especially with the advent of powerful front-end wrappers such as Keras with a TensorFlow back-end. But what if you want to identify more than one object in an image?
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In this tutorial, I’m going to show you how word vectors work. This tutorial assumes a good amount of Python knowledge, but even if you’re not a Python expert, you should be able to follow along and make small changes to the examples without too much trouble.
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This article will portray how data related to diabetes can be leveraged to predict if a person has diabetes or not. More specifically, this article will focus on how machine learning can be utilized to predict diseases such as diabetes. By the end of this article series you will be able to understand concepts like data exploration, data cleansing, feature selection, model selection, model evaluation and apply them in a practical way.
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We use big convolution kernels with large strides of four and above to detect object features on the high-resolution RGB input frame. Convolutions for layers with a small number of channels (as it is the case for the RGB input) are comparably cheap, so using big kernels here has almost no effect on the computational costs.
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