Remove Artificial Intelligence Remove Clustering Remove Deep Learning
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FPGA vs. GPU: Which is better for deep learning?

IBM Journey to AI blog

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Deep learning requires a tremendous amount of computing power.

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

The process of setting up and configuring a distributed training environment can be complex, requiring expertise in server management, cluster configuration, networking and distributed computing. Scheduler : SLURM is used as the job scheduler for the cluster. You can also customize your distributed training.

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Was ist eine Vektor-Datenbank? Und warum spielt sie für AI eine so große Rolle?

Data Science Blog

Vektor-Datenbanken sind ein weiterer Typ von Datenbank, die unter Einsatz von AI (Deep Learning, n-grams, …) Wissen in Vektoren übersetzen und damit vergleichbarer und wieder auffindbarer machen. der k-Nächste-Nachbarn -Prädiktionsalgorithmus (Regression/Klassifikation) oder K-Means-Clustering.

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Creating an artificial intelligence 101

Dataconomy

How to create an artificial intelligence? The creation of artificial intelligence (AI) has long been a dream of scientists, engineers, and innovators. With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless.

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This AI can predict genetic mutations before they happen

Dataconomy

Thanks to machine learning (ML) and artificial intelligence (AI), it is possible to predict cellular responses and extract meaningful insights without the need for exhaustive laboratory experiments. Gene set enrichment : Identify clusters of genes that behave similarly under perturbations and describe their common function.

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning Blog

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.

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How Neurosymbolic AI merges logical reasoning with LLMs

Dataconomy

This is the goal behind Neurosymbolic AI , a new approach that merges deep learning with coherence-driven inference (CDI). To maximize coherence by separating true and false statements into different clusters. If a proposition supports another , it gets a positive connection.

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