UCLA researchers found the limits of deep learning networks – distinguishing between an otter and a can opener. Nicholas Baker, a cognitive psychology graduate student, explored the behaviors of two machine learning networks known as convolutional neural networks, which are well-established machine learning networks capable of visual recognition. The study aimed to test whether the networks are able to visualize automatically in the same way that humans are able to. "Recognizing by shape is a very abstract task," Baker said. "There is a lot of symbolic processing between what you initially see in sensation and what you encode in a more durable format, and we think this might be a very hard thing to do for neural networks."

This model was trained from scratch with 5000 images (no data augmentation) and scored a dice coefficient of 0.988423 (511 out of 735) on over 100k test images. This score could be improved with more training, data augmentation, fine tuning, playing with CRF post-processing, and applying more weights on the edges of the masks. The Carvana data is available on the Kaggle website. You can easily test the output masks on your images via the CLI. You can specify which model file to use with --model MODEL.pth.

Topics covered will include an overview of language vector representations, text classification, named entity recognition, and sequence to sequence modeling approaches. An emphasis will be placed on the shape of these types of problems from the perspective of deep learning architectures. This will help to develop an intuition for identifying which neural network techniques are the most applicable to new problems that practitioners may encounter. Getting Started with Natural Language Processing in Python https://morioh.com/p/04a148fa2131 This tutorial is targeted towards those interested in either natural language processing or deep learning.

This article presents how to use NVIDIA TensorRT to optimize a deep learning model that you want to deploy on the edge device (mobile, camera, robot, car ….). As an example, think of AI-based computer vision application, they need to process each frame captured by the camera. Thus, each frame makes a forward pass through the layers of the model to compute a certain output (detection, segmentation, classification…). Whatever power your GPU has, we all want the number of frames per second (FPS) at the output to be equal to one at the input (example 24, 30 FPS…). This means that the GPU is processing each frame in real-time.

There are multiple ways of learning data science. We can go through courses, pour through books, or sift through articles. It all comes down to how much conceptual knowledge are you applying on a daily basis. That is what will improve, enhance and build your data science career (and consequently your chances of landing a data science role). Did you know that top tech behemoths open source a lot of their code on GitHub?

A'generic' implementation of EfficientNet, MixNet, MobileNetV3, etc. that covers most of the compute/parameter efficient architectures derived from the MobileNet V1/V2 block sequence, including those found via automated neural architecture search. I originally implemented and trained some these models with code here, this repository contains just the GenEfficientNet models, validation, and associated ONNX/Caffe2 export code. I've managed to train several of the models to accuracies close to or above the originating papers and official impl. More pretrained models to come... The weights ported from Tensorflow checkpoints for the EfficientNet models do pretty much match accuracy in Tensorflow once a SAME convolution padding equivalent is added, and the same crop factors, image scaling, etc (see table) are used via cmd line args.

The British Machine Vision Conference (BMVC), finished about two weeks ago in Cardiff, UK, is one of the top conferences in computer vision & pattern recognition with a competitive acceptance rate of 28%. Compared to others, it's a small event, so you have plenty of time to walk around posters and talk to presenters one-on-one, which I found really nice. I presented a poster on Image Classification with Hierarchical Multigraph Networks on which I mainly worked during my internship at SRI International under the supervision of Xiao Lin, Mohamed Amer (homepage) and my PhD advisor Graham Taylor. In the paper, we basically try to answer the question "Can we do better than Convolutional Neural Networks?". Here I discuss this question and support my arguments by results.

Horace He recently published an article summarising The State of Machine Learning Frameworks in 2019. The article utilizes several metrics to argue the point that PyTorch is quickly becoming the dominant framework for research, whereas TensorFlow is the dominant framework for applications deployed within a commercial/industrial context. He, a research student at Cornell University, counted the number of papers discusing either PyTorch or TensorFlow that were presented at a series of well-known machine learning oriented conferences, namely ECCV, NIPS, ACL, NAACL, ICML, CVPR, ICLR, ICCV and EMNLP. In summary, the majority of papers were implemented in PyTorch for every major conference in 2019. PyTorch outnumbered TensorFlow by 2:1 in vision related conferences and 3:1 in language related conferences.

"One of the holy grails of machine learning is to automate more and more of the feature engineering process" -- Pedro Domingos, CACM 2012 One of the biggest challenges in machine learning workflows is identifying which inputs in your data will provide the best signals for training predictive models. For image data and other unstructured formats, deep learning models are showing large improvements over prior approaches, but for data already in structured formats, the benefits are less obvious. At Zynga, I've been exploring feature generation methods for shallow learning problems, where our data is already in a structured format, and the challenge is to translate thousands of records per user into single records that summarize user activity. Once you have the ability to translate raw tracking events into user summaries, you can apply a variety of supervised and unsupervised learning methods to your application. I've been leveraging the Featuretools library to significantly reduce my time spent building predictive models, and it's unlocked a new class of problems that data scientists can address.

Kheiron Medical Technologies, a startup that aims to help radiologists detect breast cancer earlier by using deep learning, has announced this week the closing of a $22m Series A round led by London-headquartered VC firm Atomico. Connect Ventures, Greycroft, Hoxton Ventures and EXOR Seeds, existing backers, also participated. The startup was founded in 2016 by Peter Kecskemethy and Tobias Rijken, and its first product, called Mia (Mammography Intelligent Assessment), was created to help improve outcomes for the millions of women living with breast cancer globally. It is designed to analyse standard full-field digital mammography (FFDM) images in a breast cancer screening setting and support radiologists in making the clinical decision to recall a patient for further investigation or not. "In my childhood I spent many hours in my mother's radiology department watching her carefully read and report imaging studies and struggling with workloads and working conditions," said Dr Peter Kecskemethy, chief executive of Kheiron.