neural network

Generative malware outbreak detection

February 23, 2020

Practical defense systems require precise detection during malware outbreaks with only a handful of available samples. Sean Park demonstrates how to detect in-the-wild malware samples with a single training sample of a kind, with the help of TensorFlow's flexible architecture in implementing a novel variable-length generative adversarial autoencoder.

How Criteo optimized and sped up its TensorFlow models by 10x and served them under 5 ms

February 23, 2020

Criteo's real-time bidding of ad spaces requires its TensorFlow (TF) models to make online predictions in less than 5 ms. Nicolas Kowalski and Axel Antoniotti explain why Criteo moved away from high-level APIs and rewrote its models from scratch, reimplementing cross-features and hashing functions using low-level TF operations in order to factorize as much as possible all TF nodes in its model.

Natural language processing using transformer architectures

February 23, 2020

Transformer architectures have taken the field of natural language processing (NLP) by storm and pushed recurrent neural networks to the sidelines. Aurlien Gron examines transformers and the amazing language models based on them (e.g., BERT and GPT 2) and shows how you can use them in your projects.

About Space Invaders and automated scaling

February 22, 2020

Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.

Anomaly detection using deep learning to measure the quality of large datasets

February 22, 2020

Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data.

Zero to ML hero with TensorFlow 2.0

February 22, 2020

Get a programmer's perspective on machine learning with Laurence Moroney, from the basics all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.

Deep learning from scratch

February 15, 2020

You'll go hands-on to learn the theoretical foundations and principal ideas underlying deep learning and neural networks. Bruno Gonalves provides the code structure of the implementations that closely resembles the way Keras is structured, so that by the end of the course, you'll be prepared to dive deeper into the deep learning applications of your choice.

Deep learning methods for natural language processing

February 15, 2020

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include Word2Vec, recurrent neural networks (RNNs) and variants (long short-term memory [LSTM] and gated recurrent unit [GRU]), and convolutional neural networks.

How machine learning meets optimization

February 12, 2020

Machine learning and constraint-based optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. But Jari Koister examines how they're similar, how they're different, and how they can be used to solve complex problems with amazing results.

Deep learning at scale: Tools and solutions

February 6, 2020

Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.

Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs

February 6, 2020

Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.

Long-term real-time network traffic flow prediction using LSTM recurrent neural network

February 4, 2020

Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM.

Learn neural networks with Gonot math

January 28, 2020

Studying neural networks is a surefire way to end up fighting more math than you can shake a stick at. Wish you could learn about the likes of gradient descent and backpropagation in a language you actually understandlike Go? Then this one is for you. Join Ellen Korbes to learn neural networks with code, not math, and algorithms, not logarithms.

Deep learning for recommender systems

January 12, 2020

The success of deep learning has reached the realm of structured data in the past few years, where neural networks have been shown to improve the effectiveness and predictability of recommendation engines. Oliver Gindele offers a brief overview of such deep recommender systems and explains how they can be implemented in TensorFlow.

Predicting real-time transaction fraud using supervised learning

January 7, 2020

Predicting transaction fraud of debit and credit card payments in real time is an important challenge, which state-of-art supervised machine learning models can help to solve. Sami Niemi offers an overview of the solutions Barclays has been developing and testing and details how well models perform in variety of situations like card present and card not present debit and credit card transactions.

Artists and supercomputers: Creative collaborations in AI

January 3, 2020

What's it like to be a mobile phone or to attach a wind sensor to a neural network? Jeff Thompson outlines several recent creative projects that push the tools of AI in new directions. Part technical discussion and part case study for embedding artists in technical institutions, this talk explores the ways that artists and scientists can collaborate to expand the ways that AI can be used.

Chargrid: Understanding 2D documents

January 1, 2020

Anoop Katti explores the shortcomings of the existing techniques for understanding 2D documents and offers an overview of the Character Grid (Chargrid), a new processing pipeline pioneered by data scientists at SAP.

Deep learning methods for natural language processing

January 1, 2020

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks.

How to build privacy and security into deep learning models

December 31, 2019

In recent years, we've seen tremendous improvements in artificial intelligence, due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.

Industrialized capsule networks for text analytics

December 30, 2019

Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.

Leveraging AI for social good

December 30, 2019

The hardware, software, and algorithms that automatically tag our images or recommend the next book to read can also improve medical diagnosis and protect our natural resources. Jack Dashwood and Anna Bethke discuss a variety of technical projects at Intel that have enabled social good organizations and provide guidance on creating or engaging in these types of projects.

Recurrent neural networks for time series analysis

December 29, 2019

Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

Understanding and integrating Intel Deep Learning Boost (Intel DL Boost)

December 28, 2019

Banu Nagasundaram offers an overview of Intel's Deep Learning Boost (Intel DL Boost) technology, featuring integer vector neural network instructions targeting future Intel Xeon scalable processors. Banu walks you through the 8-bit integer convolution implementation made in the Intel MKL-DNN library to demonstrate how this new instruction is used in optimized code.

Dilated neural networks for time series forecasting

December 25, 2019

Dilated neural networks are a class of recently developed neural networks that achieve promising results in time series forecasting. Chenhui Hu discusses representative network architectures of dilated neural networks and demonstrates their advantages in terms of training efficiency and forecast accuracy by applying them to solve sales forecasting and financial time series forecasting problems.

Faster ML over joins of tables

December 24, 2019

Arun Kumar details recent techniques to accelerate ML over data that is the output of joins of multiple tables. Using ideas from query optimization and learning theory, Arun demonstrates how to avoid joins before ML to reduce runtimes and memory and storage footprints. Along the way, he explores open source software prototypes and sample ML code in both R and Python.

User-based real-time product recommendations leveraging deep learning using Analytics Zoo on Apache Spark and BigDL

December 20, 2019

User-based real-time recommendation systems have become an important topic in ecommerce. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla demonstrate how to build deep learning algorithms using Analytics Zoo with BigDL on Apache Spark and create an end-to-end system to serve real-time product recommendations.

Networking Optimizations for Multi-Node Deep Learning on Kubernetes

November 24, 2019

Training a Neural Network may take days or weeks, even on a top of the line GPU. To reduce training time, distributed computation is often employed to spread the work across multiple GPUs and multiple …

Hyperparameter Tuning Using Kubeflow

October 12, 2019

In machine learning, hyperparameter tuning refers to the process of finding the optimal constraints for training models. Choosing optimal hyperparameters can drastically improve the performance of a m …