New compliance technology such as AI and intelligent tagging has the power to change compliance. Our webinar which brought together in-house experts and external subject matter specialists has shed light on the latest cutting-edge technologies and how they can help solve the many day-to-day challenges faced by compliance professionals across the globe. In today's rapidly changing regulatory landscape, it is critically important for banks and financial institutions to respond to new regulations with agility, while ensuring that the customer experience does not suffer. These dual demands put pressure on compliance departments. Compliance technology in the form of end-to-end controls capable of mitigating a multitude of financial crime risks can help to alleviate this pressure.

Artificial intelligence has evolved to the point where any sales organization that leverages AI will see measurable improvements in customer engagement, LTV, and overall sales. Join speakers from Capital One, Yelp, SurveyMonkey, and TopBots to learn how to use AI to sell B2B smarter and harder at this VB Live event. "When we think about applying AI, we try to take a human-centered lens to it," says Rick Winslow, VP and head of digital innovation and transformation at Capital One Commercial Banking. "We want to start by asking what problems and opportunities do our customers and associates have day to day?" It just comes down to efficiency, Winslow says: Saving sales people time in finding customers, helping them increase their hit rate, enriching the data they have in order to go after the most qualified customers, and helping enrich that customer conversation.

This Neural Network tutorial will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a usecase implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain.

If you've been following along with this series of blog posts, then you already know what a huge fan I am of Keras. Keras is a super powerful, easy to use Python library for building neural networks and deep learning networks. In the remainder of this blog post, I'll demonstrate how to build a simple neural network using Python and Keras, and then apply it to the task of image classification. To start this post, we'll quickly review the most common neural network architecture -- feedforward networks. We'll then discuss our project structure followed by writing some Python code to define our feedforward neural network and specifically apply it to the Kaggle Dogs vs. Cats classification challenge.

PyTorch is a flexible open source framework for Deep Learning experimentation. In this post, you will learn how to train PyTorch jobs on Amazon SageMaker. As usual, you'll find my code on Github:) SageMaker provides a collection of built-in algorithms as well as environments for TensorFlow and MXNet… but not for PyTorch. Fortunately, developers have the option to build custom containers for training and prediction. This will require some changes in our PyTorch script, the well-known example of learning MNIST with a simple CNN.

Have you ever faced challenges in understanding language and planning sentences while performing Natural Language Processing? Do you wish to overcome these problems and go beyond the basic techniques like bag-of-words? This course is designed with advanced solutions that will take you from newbie to pro in performing Natural Language Processing with NLTK. In this course, you will come across various concepts covering natural language understanding, Natural Language Processing, and syntactic analysis. It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more.

Apple has revealed sweeping changes to the Mac platform, amid accusations of ignoring it. Computers from the MacBook Air to the iMac will benefit from new software – named Mojave, after the Californian desert – that will vastly change the look of the computers and allow them to use new kinds of apps in innovative ways. The changes were announced at WWDC, Apple's developer conference during which it updates every one of its platforms. The Mac updates came alongside new versions of its mobile operating system, iOS, and the software that powers its Apple Watch and Apple TV. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).

The strategies critical to keep business events vibrant, compelling and successful are constantly changing. Enter, the PCMA Business Event Bootcamp. This online and self-paced event course is designed to give you and/or your team unlimited access to the latest trends and critical information for successful event planning.

Statistical hypothesis tests can be used to indicate whether the difference between two samples is due to random chance, but cannot comment on the size of the difference. A group of methods referred to as "new statistics" are seeing increased use instead of or in addition to p-values in order to quantify the magnitude of effects and the amount of uncertainty for estimated values. This group of statistical methods is referred to as "estimation statistics". In this tutorial, you will discover a gentle introduction to estimation statistics as an alternate or complement to statistical hypothesis testing. A Gentle Introduction to Estimation Statistics for Machine Learning Photo by Nicolás Boullosa, some rights reserved.