Love to explore and learn new concepts. I am extremely interested in artificial intelligence, deep learning, robots, and the universe.
In this article, we take a look at some of the fundamental concepts required for constructing neural networks from scratch. This includes detailed explanations of NN layers, activation functions, and loss functions.
In this blogpost, we endeavor to build a conceptual understanding of how exactly ProGANs work. We then proceed to build the network from scratch to generate facial structures
In this article, we explore the progress that deep learning has made in the field of music in numerous tasks related to audio and signal processing. We then proceed to model and generate our own music files using pretty_midi.
This is a tutorial for conducting auditory classification within a Gradient Notebook using TensorFlow. Readers can expect to learn about the essential basic concepts of signal processing and some of the best techniques for audio classification to achieve the best desired outcomes.
In this tutorial, we show how to implement cycle GANs from scratch for image to image translation in TensorFlow using a clever combination of a ResNet generator and Patch GAN discriminator.
In this tutorial, we examine how the BERT language model works in detail before jumping into a coding demo. We then showed how to fine-tune the model for a particular text classification task.
In this tutorial, we show how to construct the pix2pix generative adversarial from scratch in TensorFlow, and use it to apply image-to-image translation of satellite images to maps.
Learn how to construct neural networks from scratch with NumPy, and simultaneously see how the internal mechanisms behind popular libraries like PyTorch and Keras are implemented.
This tutorial examines how to construct and make use of conditional generative adversarial networks using TensorFlow on a Gradient Notebook.
