Posts in tag

Neural Networks


What does it mean for a machine to learn? In a way, machines learn just like humans. They infer patterns from data through a combination of experience and instruction. In this article, we will give you a sense of the applications for machine learning and explain why Python is a perfect choice for getting started. …

While building a machine learning model, data scaling in machine learning is the most significant element through data pre-processing. Scaling may recognize the difference between a model of poor machine learning and a stronger one. Machine learning algorithm only recognizes numerical if there is a significant difference in the dimension, say few varying in tens …

Computer-based artificial intelligence can function more like human intelligence when programmed to use a much faster technique for learning new objects, say two neuroscientists who designed such a model that was designed to mirror human visual learning. In the journal Frontiers in Computational Neuroscience, Maximilian Riesenhuber, PhD, professor of neuroscience, at Georgetown University Medical Center, and …

This month, Google forced out a prominent AI ethics researcher after she voiced frustration with the company for making her withdraw a research paper. The paper pointed out the risks of language-processing artificial intelligence, the type used in Google Search and other text analysis products. Among the risks is the large carbon footprint of developing …

Scientists often train computers to learn in the same way that young children do – by setting them loose and letting them play with themselves. Kids interact with their environments, play games on their own, and gradually get better at doing things. Many artificial intelligence (AI) systems tout their ability to “learn from scratch,” but …

You might have seen a recent article from The Guardian written by “a robot”. Here’s a sample: I know that my brain is not a “feeling brain”. But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My …

For all the progress researchers have made with machine learning in helping us doing things like crunch numbers, drive cars and detect cancer, we rarely think about how energy-intensive it is to maintain the massive data centers that make such work possible. Indeed, a 2017 study predicted that, by 2025, internet-connected devices would be using 20 …

TF Dev Summit ‘19 | Mesh-TensorFlow: Model Parallelism for Supercomputers Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency …

Artificial neural networks were created to imitate processes in our brains, and in many respects – such as performing the quick, complex calculations necessary to win strategic games such as chess and Go – they’ve already surpassed us. But if you’ve ever clicked through a CAPTCHA test online to prove you’re human, you know that …

PyCon 2019 | Put Down The Deep Learning: When Not To Use Neural Networks And What To Do Instead Speaker: Rachael Tatman   The deep learning hype is real, and the Python ecosystem makes it easier than ever to neural networks to everything from speech recognition to generating memes. But when picking a model architecture …