I bet most of us have seen a lot of AI-generated people faces in recent times, be it in papers or blogs. We have reached a stage where it is becoming increasingly difficult to distinguish between actual human faces and faces that are generated by Artificial Intelligence. In this…
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.
Doing cool things with data!
In this article, I will present five techniques to prevent overfitting while training neural networks.
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many…
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.
Knowledge distillation is a model compression technique whereby a small network (student) is taught by a larger trained neural network (teacher). The smaller network is trained to behave like the large neural network. This enables the deployment of such models… Continue reading…
Detailed derivations and open-source code to analyze the receptive fields of convnets.
With recent developments in deep learning, neural networks are getting larger and larger. For example, in the ImageNet recognition challenge, the winning model, from 2012 to 2015, increased in size by 16 times. And in just one year, for Baidu’s… Continue reading The 5 Algorithms…
Easy Explanation!!! I tried
Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods. - MrSyee/pg-is-all-you-need
A new method that provides accurate, real-time, computer-aided diagnosis of colorectal cancer identified tumors with 100% accuracy in a new pilot study.
Researchers have shrunk state-of-the-art computer vision models to run on low-power devices. Growing pains: Visual recognition is deep learning’s strongest skill. Computer vision algorithms are analyzing medical images, enabling self-driving cars, and powering face recognition…
This guide explores research centered on a variety of advanced loss functions for machine learning models.
Engineering at Forward | UCLA CS '19
Week Two - 100 Days of Code Challenge
In this tutorial you will learn how to use Keras, Mask R-CNN, and Deep Learning for instance segmentation (both with and without a GPU).
The purpose of this post is to provide guidance on which combination of final-layer activation function and loss function should be used in…
In this article, you will explore how you can leverage Kubernetes, Tensorflow and Kubeflow to scale your models without having to worry about scaling the infrastructure.
Contour detection and having a little bit of fun
What we'd like to find out about GANs that we don't know yet.
You only look once (YOLO) is a state-of-the-art, real-time object detection system.
Medallia's text analytics software tool provides actionable insights via customer and employee experience sentiment data analysis from reviews & comments.
Nvidia's GauGAN tool has been used to create more than 500,000 images, the company announced at the SIGGRAPH 2019 conference in Los Angeles.
27K votes, 533 comments. 21M subscribers in the dataisbeautiful community. DataIsBeautiful is for visualizations that effectively convey information…
A technical report on convolution arithmetic in the context of deep learning - vdumoulin/conv_arithmetic
Learn how SymphonyAI’s financial crime prevention solutions quickly deploy to uncover your risks, improve investigations, and transform your operations.
Scientists are developing AI systems called deep neural nets that can read medical images and detect disease — with astonishing efficiency
Algorithms off the convex path.
Over the last few years we have detailed the explosion in new machine learning systems with the influx of novel architectures from deep learning chip
Here we are again! We already have four tutorials on financial forecasting with artificial neural networks where we compared different…
Recurrent neural networks are a type of neural network where the outputs from previous time steps are fed as input to the current time step. This creates a network graph or circuit diagram with cycles, which can make it difficult to understand how information moves through the…
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the…
Quantized word vectors that take 8x-16x less space than regular word vectors - agnusmaximus/Word2Bits
Tensorflow (Python API) implementation of Deep Photo Style Transfer - LouieYang/deep-photo-styletransfer-tf
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while...
An open source framework for configuring, building, deploying and maintaining deep learning models in Python.
Generative adversarial networks (GANs) are a class of neural networks that are used in unsupervised machine learning. They help to solve such tasks as image generation from descriptions, getting high resolution images from low resolution ones, predicting which drug could treat a…
Through a human’s eyes, the world is much more than just the images reflected in our corneas. For example, when we look at a building and admire the intricacies of its design, we can appreciate...
Recurrent Neural Network - A curated list of resources dedicated to RNN - kjw0612/awesome-rnn
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.