PreetiYadav·22 hours agoHypothesis in Machine LearningWritten by: Preeti Yadav(201550105, GLA University) Hypothesis: Hypothesis in machine learning is a cornerstone of the subject. It is one of the most fundamental problems that machine learning research has to tackle. And it provides significant insights into the nature of different types of data, for instance, whether or not…Neural Networks3 min read
Takuma Yamaguchi (Kumon)·2 hours agoInstant NeRF on Google Compute Engine via Chrome Remote DesktopRendering a 3D NERF Toy Gun with Neural Radiance Fields (NeRF) on a Google Cloud VM — Introduction Neural radiance field (NeRF) synthesizes novel views of complex scenes using a simple fully connected neural network based on a collection of 2D images. The paper, Representing Scenes as Neural Radiance Fields for View Synthesis, was presented in ECCV 2020 and won best paper honorable mention. Their project web page…Neural Networks7 min read
Michael Campbell·1 day agoSpeculation on a new Activation Function for Deep Learning AlgorithmsThe non-linearity applied to each layer of a neural network is important for the learning of complex mappings from input to output, but the activation functions that I see typically used in deep learning (sigmoid, tanh, relu, leaky-relu, etc.) …Neural Networks7 min read
Cameron WolfeinTowards Data Science·1 day agoMember-onlySaga of the Lottery Ticket HypothesisHow winning tickets were discovered, debunked, and re-discovered — The Lottery Ticket Hypothesis (LTH) is related to neural network pruning and can be concisely summarized via the following statement [1]: “dense, randomly-initialized, feed-forward networks contain subnetworks (winning tickets) that — when trained in isolation — reach test accuracy comparable to the original network in a similar number of iterations.”Neural Networks11 min read
Sam Bennett·1 day agoBUILDING A FOOTBALL NEURAL NETCREATING A HOMEMADE NEURAL NET FOR FOOTBALL USING MATH AND YOUTUBE For me it seemed that the use of Tensorflow and Pytorch to build neural network seemed too detatched from the math for me to gain a deep understanding of what my neural net is actually doing. I have always…Neural Networks2 min read
Leah Nagy·1 day agoYoga Pose Classification With TensorFlow’s MoveNet ModelA pose-detection project using convolutional neural networks and transfer learning to correctly identify various yoga poses Since the beginning of the Covid-19 pandemic, the online fitness trend has exploded. That’s led to a surge of research into interactive AI coaches to offer user’s an interactive experience in the comfort of…Neural Networks9 min read
Nicole Janeway Bills·3 days agoMember-onlyIs the “Chartered Data Scientist” Worth It?The U.S. Bureau of Labor Statistics predicts Data Science will see more growth than almost any other field between 2022 and 2029. To set yourself apart in this crowded marketplace, consider the Chartered Data Scientist designation from Association of Data Scientists (ADaSci). — The Chartered Data Scientist (CDS) is perhaps the most well-known Data Science certification. With approximately 15% of Data Science job postings in the United States requesting certification, employers are increasingly looking at the CDS as a method for identifying the best qualified Data Scientists. Here’s some further information on the…Neural Networks5 min read
Rafay Qayyum·2 days agoIntroduction to Pooling Layers in CNNA Convolutional neural network(CNN) is a special type of Artificial Neural Network that is usually used for image recognition and processing due to its ability to recognize patterns in images. It eliminates the need of extracting features from visual data manually. It learns images by sliding a filter of some…Neural Networks5 min read
Recogni Inc.·2 days agoRECOGNI DELIVERS 1000 TOPS (MICROPROCESSOR REPORT)Driving autonomy inherently requires visual inputs which in turns necessitates a massive amount of computation. Seeing both far and near and in every direction is imperative at high speeds and with minimal processing delay. While high compute requirements typically goes hand-in-hand with high power consumption, Recogni has effectively resolved these…Neural Networks2 min read
Harjot Kaur·2 days agoUnderstanding Backpropagation using Mountaineering as an analogyIntuition behind the algorithm! Backpropagation algorithm is the workhorse of learning in the neural networks. It is an expression for the partial derivative(∂L/∂w) of the loss function(∂L) with respect to any weight w (or bias b) in the network. The expression tells us how quickly the loss changes when we…Neural Networks4 min read
