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Swish Activation function
The smooth and non-monotonic function that can be used in place of the commonly used ReLU activation function. Introduction to Activation Functions In machine learning and deep learning, an activation function is a mathematical function that is applied to the output of a neural network layer to introduce non-linearity into the network, allowing it to…
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Shout out to GAN (Start ver.)
Writing after a long time, Here’s the basic + in-depth mix explanation of Generative adversarial networks and Style-Based Generative Adversarial networks. Introduction GAN is simply a generative model that generates new data from the input data. They are used to perform unsupervised operations. They work majorly with image data and also audio data. The Generative…
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Quantum neural networks | Effortless reading
We gonna explore Quantum neural networks (QNN) in a much simplified manner, covering all the fundamentals concepts that will create a grasping impact. I’ll try making you understand with least mathematics to get a better overview as a beginner. Introduction Artificial Neural Networks (ANN) has been a generous algorithm on which the whole deep learning…
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VAE in no time
A quick tour of Variational Autoencoder (VAE) In recent times the generative model has gained huge attention due to its state-of-art performance and hence achieved massive importance in the marketplace and is also used widely. Variational Autoencoders are deep learning techniques used to learn the latent representations they are one of the finest approaches to unsupervised learning. VAE shows exceptional…
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Retirement of convolutions
Introduction “Computer Vision”, a field of Artificial Intelligence that helps Machines to visualize this beautiful world. Computer vision has led to wonders in enhancing Artificial Intelligence. From pattern recognition to Human Pose estimation, And from Robot navigation to solid-state physics, computer vision has much more useful and helpful applications. Using computer vision and deep learning, we successfully…
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