"While there have been advances in artificial intelligence (AI) this year, it's poised to skyrocket in 2019.." So a dozen articles a week begin. But what does this mean to me? I basically understand that "General AI" or computers that can comprehend a wide range of topics and interests, just like people do, is still a long way off However, Machine Learning is here right now. Teaching a model with real-world data and then predicting events using statistics is viable. I don't pretend to understand the ins and outs of the complex mathematics that underpin machine learning.

Deep learning neural networks are relatively straightforward to define and train given the wide adoption of open source libraries. Nevertheless, neural networks remain challenging to configure and train. In his 2012 paper titled "Practical Recommendations for Gradient-Based Training of Deep Architectures" published as a preprint and a chapter of the popular 2012 book "Neural Networks: Tricks of the Trade," Yoshua Bengio, one of the fathers of the field of deep learning, provides practical recommendations for configuring and tuning neural network models. In this post, you will step through this long and interesting paper and pick out the most relevant tips and tricks for modern deep learning practitioners. Practical Recommendations for Deep Learning Neural Network Practitioners Photo by Susanne Nilsson, some rights reserved.

Recently, I did the micro course Machine Learning Explainability on kaggle.com. I can highly recommend this course as I have learned a lot of useful methods to analyse a trained ML model. For a brief overview of the topics covered, this blog post will summarize my learnings. The following paragraphs will explain the methods Permutation Importance, Partial Dependence Plots and SHAP Values. I will illustrate the methods using the famous Titanic dataset.

TLDR; This series is based on the work detecting complex policies in the following real life code story. Code for the series can be found here. In the previous tutorials we outlined our policy classification challenge and showed how we can approach it using the Custom Vision Cognitive Service. This tutorial introduces deep transfer learning as a means to leverage multiple data sources to overcome data scarcity problem. Before we try to build a classifier for our complex policy let's first look at the MNIST dataset to better understand key image classification concepts such as One Hot Encoding, Linear Modeling, Multi Layer Perception, Masking and Convolutions then we will put these concepts together and apply them to our own dataset.

In this latest Data Science Central podcast, we will continue to explore the hot topic of AI and Deep Learning at SAS. We will discuss how the SAS platform enables the development of AI applications and how SAS customers are using this exciting technology today.

In this latest Data Science Central podcast, we will introduce you to the hot topic of AI and Deep Learning at SAS. We will cover what is AI, what capabilities SAS offers, and how data scientists can leverage these deep learning capabilities.

The technology is changing the world rapidly. Employees are frequently worried about their jobs will be taken by AI and other kinds of advanced technology. It is actually happening, just you don't realise it yet! The demand for certain traditional and manual jobs will decline. Instead, new skills will be required to suit the new workplace environment.

Artificial Intelligence and Machine Learning (ML) can turn massive amounts of data into deep insights that drive revenue and decrease costs. But ML's not an island – in fact, it's carried out most successfully when paired with advanced analytics. To facilitate the best analytics work, enterprises need the right platforms and tools to load data, prepare it, ensure high-quality and integrate with corporate data governance processes. How can you get all that working harmoniously, especially in the cloud? It takes the right tools, strategy and workflow, but it can be done. Watch this 1-hour on-demand webinar, from GigaOm Research, to find out how.

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Use coupon code ALMOSTFREE and get FLAT 95% discount Learn how to organize your data with the popular Neo4j graph database in this Neo4J database tutorial!! Search engines and social media platforms have propelled graph databases into the lime light. While traditional relational databases are still popular among many companies, graph databases are slowly climbing the ranks as a go to database for many complex structures. Databases play an important role when it comes to storing and fetching large amounts of data. Data is often a huge mess on the internet, which needs to be meticulously sorted into sections and sub-sections to make it easier for analyzing. Data is in raw form is useless for individuals and companies alike, until it is sorted and provides the user with information or it can specifically answer the user's question.