LoRA - Low Rank Adaptation
In the last part of Compression of NN[1], we delved into methods for reducing the memory footprint of large ML models, in this part let's look into how to fine-tune these large models in consumer grade resources with...
ml
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Unconditional image generation
In the realm of artistry, where creativity and imagination intertwine, a captivating scene often begins as a humble sketch on paper. Though fascinating in their monochrome simplicity, these lines and contours often le...
Unconditional image generation
I know I'm late for the party. It's been a long time since the diffusion models have been introduced. There have been massive improvements in image generation since I have known image generation with ML, from GANs to...
Breaking Bits, a dive into bitwise NOT (~)
I was working on an ML model and I stumbled on the task of writing my loss function. Interestingly, I had to use a bitwise operator, among other operations, on an image. To convert it into a sketch. As I blindly plug...
Compression of deep neural networks: P-1
Since the inception of the Deep Neural Network, their complexity has been increasing. For a small comparison, look at the table below [1]. ArchitectureModel parameters (in Millions)Alex-Net60Basic-L2440 Alex-Net w...