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Machine Learning Interview Questions to Land the Perfect Data Science Job

Smart Data Collective

The Bureau of Labor Statistics reports that there were over 31,000 people working in this field back in 2018. Is K-means clustering different from KNN? You can also use your knowledge of big data to create AI algorithms that will prevent fraud in games that involve spending money. Are you looking to get a job in big data?

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

Our high-level training procedure is as follows: for our training environment, we use a multi-instance cluster managed by the SLURM system for distributed training and scheduling under the NeMo framework. Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms.

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We still have so much to learn from nature

Dataconomy

The Kilobot platform provides researchers with a practical means to study and experiment with swarm robotics algorithms and concepts. Swarm intelligence algorithms are typically decentralized, meaning that they do not require a central controller. The robots were able to plant the rice more quickly and efficiently than human workers.

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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete. Another way can be to use an AllReduce algorithm.

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. The following figure illustrates the Neuron software stack.

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Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). If you have a large dataset, the SageMaker KNN algorithm may provide you with an effective semantic search.

Algorithm 101