Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.

We study the problem of protecting information when learning with graph-structured data. While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representational learning in many applications, the neighborhood aggregation paradigm exposes additional vulnerabilities to attackers seeking to extract node-level information about sensitive attributes. To counter this, we propose a minimax game between the desired GNN encoder and the worst-case attacker. The resulting adversarial training creates a strong defense against inference attacks, while only suffering a small loss in task performance. We analyze the effectiveness of our framework against a worst-case adversary, and characterize the trade-off between predictive accuracy and adversarial defense. Experiments across multiple datasets from recommender systems, knowledge graphs and quantum chemistry demonstrate that the proposed approach provides a robust defense across various graph structures and tasks, while producing competitive GNN encoders. Our code is available at https://github.com/liaopeiyuan/GAL.

Online Courses Udemy The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Advanced AI: Deep Reinforcement Learning in Python Python for Data Science and Machine Learning Bootcamp The Complete Python Course Learn Python by Doing Complete Python Web Course: Build 8 Python Web Apps The Complete Python Masterclass: Learn Python From Scratch Preview this course GET COUPON CODE Description This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

This repository contains the code for the blog post: Using Microsoft AI to Build a Lung-Disease Prediction Model using Chest X-Ray Images, by Xiaoyong Zhu, George Iordanescu, Ilia Karmanov, data scientists from Microsoft, and Mazen Zawaideh, radiologist resident from University of Washington Medical Center. You should be able to run the code from scratch and get the below result using Azure Machine Learning platform or run it using your own GPU machine. If you are using Azure Machine Learning as the training platform, all the dependencies should be installed. However, if you are trying out in your own environment, you should also install keras-contrib repository to run Keras code. If you are trying out the lung detection algorithm, you need to install a few other additional libraries.

We previously covered Charmed Labs PIXY2 computer vision camera based on an NXP LPC4330 microcontrollers that worked with Arduino, Raspberry Pi, and other development boards. The company is now back with a fully integrated more powerful solution with Vizy AI camera featuring a Raspberry Pi 4 SBC with up to 8GB RAM. The AI camera supports optional accessories including an M12 zoom lens with 87 FoV, an outdoor enclosure, lighting, a telescope adapter, and a 4G LTE cellular USB modem. Vizy can be configured via a web interface accessible from any device or computer with a web browser and programmed using Python with support for deep-learning neural networks in order to detect objects, people and/or animals using Tensorflow, OpenCV, or PyTorch. As an edge AI platform, all processing is done onboard, which means no connection to the cloud is needed nor a monthly fee has to be paid.

More than a year since they announced a partnership to advance machine learning advancement in areas such as oncology, precision medicine and imaging, the researchers of the Pittsburgh Health Data Alliance and AWS, are unveiling new AI-based techniques to assess breast cancer risk, understand tumor growth and better spot markers of depression. In one project, a team in the radiology department at University of Pittsburgh are using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer and develop a more personalized approach for patients undergoing screening. Researchers gathered more than 450 de-identified normal screening mammogram images from 226 patients, half of whom later developed breast cancer and half of whom did not. With help from AWS tools, they developed two different machine learning models to analyze the images for characteristics that could help predict breast cancer risk. Both outperformed the simple measure of breast density, which today is the primary imaging marker for breast cancer risk.

The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomogra-phy is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specic materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. The network is trained to decompose the materials in the projection domain after which we apply any conventional tomographic method to reconstruct the dierent material volumes. The proposed decomposition method is compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data.

Taught by: Rachel Thomas is an American computer scientist and founding Director of the Center for Applied Data Ethics at the University of San Francisco. Together with Jeremy Howard, she is co-founder of fast.ai. Course Outcomes: This course is a hands-on introduction to NLP, where you will code a practical NLP application first as the name suggests, then slowly start digging inside the underlying theory in it. Applications covered include topic modeling, classification (identifying whether the sentiment of a review is positive or negative), language modeling, and translation. The course teaches a blend of traditional NLP topics (including regex, SVD, naïve Bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the transformer architecture), as well as addressing urgent ethical issues, such as bias and disinformation.

The ongoing trend of building ever larger models like BERT and GPT-3 has been accompanied by a complementary effort to reduce their size at little or no cost in accuracy. Effective models are built either via distillation (Pre-trained Distillation, DistilBERT, MobileBERT, TinyBERT), quantization (Q-BERT, Q8BERT) or parameter pruning. On September 27, Huawei introduced TernaryBERT, a model that leverages both distillation and quantization to achieve accuracy comparable to the original BERT model with 15x decrease in size. What is truly remarkable about TernaryBERT is that its weights are ternarized, i.e. have one of three values: -1, 0, or 1 (and can hence be stored in only two bits). The paper heavily references prior work and is therefore quite dense.

Learn Data Science, Deep Learning, Machine Learning, NLP & R Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0. 4 hours Play preview ... Introduction to Natural Language Processing in Python. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. What Does a Data Scientist Do? In the past decade, data scientists have become necessary assets and are present in almost all organizations.