FeaturedUdemy
Google DeepMind: Accelerate Your Model
5.0(20)107 studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate Google DeepMind course provides practical insights into optimizing GPU hardware efficiency during model training. Learners explore memory estimation, compute reduction techniques, and fine-tuning a 4-billion parameter Gemma model while examining the environmental and ethical impacts of AI computing.
Who It's For
Machine learning engineers and data scientists seeking to optimize hardware utilization and fine-tune large language models efficiently on single-GPU hardware.
Key Takeaways
- Compute & Memory Estimation: Calculate compute and memory requirements necessary for training large neural networks.
- Model Acceleration & Fine-Tuning: Apply memory reduction techniques to fine-tune a 4B-parameter Gemma model on single-GPU setups.
- Environmental & Social Impact: Analyze energy, water usage, e-waste, and environmental justice considerations in machine learning.
About this course
Train more powerful models with a single GPU. In this course, you will learn how hardware can speed up model training and the key considerations when training models on a GPU. First, you will learn how to estimate the number of computations and the amount of computer memory required to train large neural networks. You will then discover techniques for reducing the computing and memory requirements when training a model. Techniques which you will apply for fine-tuning a Gemma model with 4 billion parameters. Finally, you will consider the potential environmental impacts of machine learning, with a focus on where questions of energy, water, and e-waste intersect with justice and equity.
Price
Free
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