Oh yeah completely – I'm so embedded in engineering I forget there are startups where the technology isn't the differentiator!
AI
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Career Growth: Startups vs Large Organizations for New Engineers
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I guess the key thing is more your career stage – if you're brand new to the industry a startup is less likely to be able to offer you the mentoring and support you need to grow than a larger organization, especially one with experience of training up new engineers
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T-shaped developers thrive in self-guided learning environments
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Not so good if you want to specialize in a specific area (eg iOS) and be mentored by senior developers, but it's a fantastic environment for people who want to be more T-shaped and who are good at self-guided learning
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Engineer Skills Development at Startups Across Full Stack
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That doesn't match my experience At a small startup an engineer might get to work on ops, backend and frontend code – whatever technical problems need solving Great for gaining a wide range of skills
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Unlocking AI’s Full Potential for Human Intelligence
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We're Not Using AI to Its Fullest Human Potential https://
time.com/6227118/eric-s
chmidt-ai-human-intelligence/
… @TIME #AI #MachineLearning #DataScience #BigData #Analytics #100DaysofCode #IoT #serverless #womenwhocode #DigitalTransformation #Robots #Python #TensorFlow #DeepLearning -
NVIDIA DGX BasePOD AI Infrastructure Solutions at NetApp INSIGHT
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At #NetAppINSIGHT 2022, explore the latest on AI infrastructure solutions powered by NVIDIA DGX BasePOD built on NVIDIA DGX systems with AMD EPYC CPUs: https://
nvda.ws/3SZc7Mc -
Technical Debt and Architectural Complexity in Software Systems
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I imagine this is more about simplicity at a small scale, which then gets baked in too deep to easily refactor layer on
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Isolate Analytics Data From Core Application Databases
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This makes a ton of sense to me: keep less critical, high throughput data such as analytics in a separate database from your core application data such that any problems with the high throughput stuff are isolated from affecting your key tables
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Adaptive Optimizers Should Track True Squared Gradients
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Adaptive optimizers like Adam track the square of the gradient, but what they receive as the gradient is actually the sum of the gradients across the batch. It seems likely that better results at different hyper parameters could be obtained if backward passes emitted true grad^2.
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Detecting Non-Gaussian Distributions in Machine Learning Models
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The easiest way to see this is to fix the mean to be 0 and then observe that the returned value will never be negative. Thus it can't be Gaussian. Definitely not easy to spot.