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Quantization in Deep Learning (LLMs)
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This video is about quantization in deep learning in the deep learning tutorial series.
Quantization is getting more and more popular and essential to deal with the ever-growing deep learning models. But how does quantization work? What are the different types of quantization algorithms? What are the different models of quantization? I have tried to answer these questions in this video.
Topics covered include Types of Quantization - Uniform and Non-Uniform quantization, and further divisions of Uniform quantization such as symmetric and asymmetric quantization, dequantization, choosing the scale factor and zero point parameters for both symmetric and asymmetric quantization. Lastly, Post-training quantization or PQT and Quantization Aware Training or QAT are also covered.
A practical guide to neural network quantization both in PyTorch and TensorFlow is to follow.
As always, hope it's useful!
RELATED LINKS
AI BITES LINKS
🛠 🛠 🛠 MY SOFTWARE TOOLS 🛠 🛠 🛠
📚 📚 📚 BOOKS I HAVE READ, REFER AND RECOMMEND 📚 📚 📚
WHO AM I?
I am a Machine Learning Researcher / Practioner who has seen the grind of academia and start-ups equally. I started my career as a software engineer 15 years back. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution just started. Life has changed for the better ever since.
#machinelearning #deeplearning #aibites
Quantization is getting more and more popular and essential to deal with the ever-growing deep learning models. But how does quantization work? What are the different types of quantization algorithms? What are the different models of quantization? I have tried to answer these questions in this video.
Topics covered include Types of Quantization - Uniform and Non-Uniform quantization, and further divisions of Uniform quantization such as symmetric and asymmetric quantization, dequantization, choosing the scale factor and zero point parameters for both symmetric and asymmetric quantization. Lastly, Post-training quantization or PQT and Quantization Aware Training or QAT are also covered.
A practical guide to neural network quantization both in PyTorch and TensorFlow is to follow.
As always, hope it's useful!
RELATED LINKS
AI BITES LINKS
🛠 🛠 🛠 MY SOFTWARE TOOLS 🛠 🛠 🛠
📚 📚 📚 BOOKS I HAVE READ, REFER AND RECOMMEND 📚 📚 📚
WHO AM I?
I am a Machine Learning Researcher / Practioner who has seen the grind of academia and start-ups equally. I started my career as a software engineer 15 years back. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution just started. Life has changed for the better ever since.
#machinelearning #deeplearning #aibites
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