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ML

DeepFake Detection Methods
·1162 words·6 mins
In this blog post, we explore the topic of image generators and their detection techniques. I’ll discuss various methods for detecting image generators and their manipulations. These include analyzing the visual content of an image, examining its metadata, and using machine learning algorithms to identify patterns in the data.
From CNNs to Vision Transformers: The Future of Image Recognition
·6015 words·29 mins
Vision Transformers (ViTs) are redefining image recognition by using Transformer models to capture global context, unlike traditional Convolutional Neural Networks (CNNs) that focus on local features. ViTs excel with large datasets and show impressive scalability and performance.
imageNet-Computer Vision Backbone
·1065 words·5 mins
ImageNet is more than just a dataset. The sheer scale of ImageNet, combined with its detailed labeling, made it essentially the backbone of Computer Vision.
Transformers & Attention
·866 words·5 mins
This blog post explains how self-attention and softmax function in Transformer models, crucial for modern NLP. It breaks down how self-attention helps models understand relationships between tokens and how softmax ensures efficient computation and numerical stability.
Diffusion VS Auto-Regressive Models
·1085 words·6 mins
Generative AI has come a long way, producing stunning images from simple text prompts. But how do Diffusion and Auto-Regressive models work, and why are diffusion models preferred.
AlexNet Revolution
·1304 words·7 mins
In 2012, the field of artificial intelligence witnessed a seismic shift. The catalyst for this transformation was a deep learning model known as AlexNet.
Generative Adversarial Network
·753 words·4 mins
A neural network is like a highly sophisticated, multi-layered calculator that learns from data. It consists of numerous “neurons” (tiny calculators) connected in layers, with each layer performing a unique function to help the network make predictions or decisions.
Variational-Auto-Encoder
·729 words·4 mins
The beauty of VAEs lies in their ability to generate new samples by randomly sampling vectors from this known region and then passing them through the generator part of our model.
Auto-Encoder
·545 words·3 mins
An autoencoder begins its journey by compressing input data into a lower dimension. It then endeavors to reconstruct the original input from this compressed representation.
Less is More Paper Review
·467 words·3 mins
Less is More: Parameter-Free Text Classification with Gzip offers a novel text classification method using gzip compression, eliminating manual parameter tuning.