In this article, we explore what global average and max pooling entail. We discuss why they have come to be used and how they measure up against one another. We also developed an intuition into why they work by performing a biopsy of our convnets and visualizing intermediate layers.
In this deep dive of BERT, we explore the powerful NLP model's history, break down the approach and architecture behind the model, and take a look at some relevant experiments. We then close with a code demo showing how to use BERT, DistilBERT, RoBERTa, and ALBERT in a Gradient Notebook.
In this blog post we examine the growing technology of weakly supervised learning, in the context of other machine/deep learning techniques, and discuss some of the potential applications and frameworks that make use of them.
In this blog post we take an in depth look at the Transformer model architecture, and demo its functionality by rebuilding the model from scratch in Python.
In this blog post we explore the differences between deed-forward and feedback neural networks, look at CNNs and RNNs, examine popular examples of Neural Network architectures, and their use cases.
In this blog, we discuss various types of learning paradigms present in NLP, notations often used in the prompt-based learning paradigm, demo applications of prompt-based learning, and discuss some design considerations to make while designing a prompting environment.
We couldn't pass up the chance to talk to Rohan Tondulkar, a Senior Research Scientist at SciSpace using machine learning to make scientific literature more approachable and collaboration more accessible.
In this tutorial we show how to collect image data from the web to use in your computer vision, deep learning projects on Paperspace.
In this blog post, we examine Captum, which supplies academics and developers with cutting-edge techniques, such as Integrated Gradients, that make it simple to identify the elements that contribute to a model's output. We then put these techniques to use in a coding demo with ResNet.
