The below is a summary of my recent article on neuromorphic computing. Neuromorphic computing is a new field of artificial intelligence (AI) that aims to mimic the structure and function […] The post Revolutionary Neuromorphic Computing: The Key to Hyper-Realistic Generative AI appeared first on Datafloq.
March 13, 2023, 2:28 a.m.
In this video presentation, Mohammad Namvarpour presents a comprehensive study on Ashish Vaswani and his coauthors' renowned paper, “Attention Is All You Need.” This paper is a major turning point in deep learning research. The transformer architecture, which was introduced in this paper, is now used in a variety of …
In this contributed article, Al Gharakhanian, Machine Learning Development Director, Cognityze, takes a look at anomaly detection in terms of real-life use cases, addressing critical factors, along with the relationship with machine learning and artificial neural networks.
Today’s artificial intelligence technologies use quite a lot of energy. For this reason, the production of low-energy AI systems is very important for a sustainable world. Artificial intelligence may be performed using tiny nanomagnets that communicate similarly to neurons in the brain, according to researchers. The Imperial College London researchers
In this regular column, we take a look at highlights for important research topics of the day for big data, data science, machine learning, AI and deep learning. It’s important to keep connected with the research arm of the field in order to see where we’re headed. In this edition, …
Whether you implement a neural network yourself or you use a built in library for neural network learning, it is […] The post A Gentle Introduction To Sigmoid Function appeared first on Machine Learning Mastery.
In this contributed article, editorial consultant Jelani Harper points out that a generous portion of enterprise data is Euclidian and readily vectorized. However, there’s a wealth of non-Euclidian, multidimensionality data serving as the catalyst for astounding machine learning use cases.
We are at an interesting time in our industry when it comes to validating models – a crossroads of sorts when you think about it. There is an opportunity for practitioners and leaders to make a real difference by championing proper model validation. That work has to include interpretability and …
March 16, 2021, 9:34 a.m.
In this contributed article, Pippa Cole, Science Writer at the London Institute for Mathematical Sciences, discusses new research on artificial neural networks that has added to concerns that we don’t have a clue what machine learning algorithms are up to under the hood. She highlights a new study that focuses …
A group of AI researchers from DarwinAI and out of the University of Waterloo, announced an important theoretical development in deep learning around "attention condensers." The paper describing this important advancement is: "TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices," by Alexander Wong, et al. Wong …
Introduction With the Databricks Runtime 7.2 release, we are introducing a new magic command %tensorboard. This brings the interactive TensorBoard experience Jupyter notebook users expect to their Databricks notebooks. The %tensorboard command starts a TensorBoard server and embeds the TensorBoard user interface inside the Databricks notebook for data scientists and …
Machine learning including deep learning feels like something right out of a science fiction story, and it’s here to be utilized. Deep learning extracts patterns from all sorts of data including images, and the following will help you understand how this happens. How Deep Learning Works? You should understand how …
Brandon Rohrer is an expert in neural networks and deep learning. Plus, he makes really excellent videos about the topic. His entire YouTube Channel is worth viewing. https://youtu.be/ILsA4nyG7I0 How Deep Neural Networks Work by Brandon Rohrer See other top data science videos on the Data Science 101 video page. The …
April 6, 2020, 12:20 p.m.
Brandon Rohrer is an expert in neural networks and deep learning. Plus, he makes really excellent videos about the topic. His entire YouTube Channel is worth viewing. The post Brandon Rohrer – How Deep Neural Networks Work appeared first on Data Science 101.
April 6, 2020, 12:20 p.m.
Will a network trained with fake data be able to generalize to the real world?Lauren Holzbauer was an Insight Fellow in Summer 2018.Today, I don’t think twice about walking into any gym, assessing the equipment, and throwing a really good workout together, but it hasn’t always been that way. The …
Let’s use our ninja skills to figure out what CNNs are really doing.Lauren Holzbauer was an Insight Fellow in Summer 2018.By this time, many people know that the convolutional neural network (CNN) is a go-to tool for computer vision. But why exactly are CNNs so well-suited for computer vision tasks, …
Get the theory behind neural networks straight once and for all!A 2-layer “vanilla” Neural Network.Lauren Holzbauer was an Insight Fellow in Summer 2018.In my last post, we went back to the year 1943, tracking neural network research from the McCulloch & Pitts paper, “A Logical Calculus of Ideas Immanent in …
Wow, the last two weeks were taken over by the flurry of announcements from Amazon. Even though Amazon is taking a break from announcements (probably focusing on Christmas shoppers), there are still some updates in the cloud data science world. Here they are. News Google AutoML for Natural Language goes …
