Remove Deep Learning Remove Modeling Remove Optimization Remove Testing
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A Practitioner’s Guide to Deep Learning with Ludwig

Domino Data Lab

New tools are constantly being added to the deep learning ecosystem. For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research.

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Running Code and Failing Models

DataRobot

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

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DataRobot is Acquiring Algorithmia, Enhancing Leading MLOps Infrastructure to Get Models to Production Fast, with Optimized GPU Workloads at Scale

DataRobot

In a global marketplace where decision-making needs to happen with increasing velocity, data science teams often need not only to speed up their modeling deployment but also do it at scale across their entire enterprise. This allows for the pipelining of incredibly complex inference models.

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Deep learning for improved breast cancer monitoring using a portable ultrasound scanner

Insight

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The box plot below shows a summary of the testing results.

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You Can Optimize GPT If You Understand its Limitations!

Smarten

GPT, or Generative Pre-Trained Transformer, is a Large Language Model (LLM). If we are to safely and securely optimize its potential, GPT must be managed as it evolves. But it is crucial to understand the current state of AI and GPT and its limitations. Its analytical and mathematical skills are still in question.

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12 data science certifications that will pay off

CIO Business Intelligence

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric.

Marketing 362