The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.
Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.
Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.
Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.
Also: What is it like to be a machine learning engineer in 2018?; 7 Simple Data Visualizations You Should Know in R; Choosing the Right Metric for Evaluating Machine Learning Models - Part 2; Data Lake - the evolution of data processing
A single query optimization tip can boost your database performance by 100x. Although we usually advise our customers to use these tips to optimize analytic queries (such as aggregation ones), this post is still very helpful for any other type of query.
In this blog I am going to talk about the issues related to initialization of weight matrices and ways to mitigate them. Before that, let’s just cover some basics and notations that we will be using going forward.
Also: Cartoon: 5 Machine Learning Projects You Should Not Overlook, June 2018; FIFA World Cup Football and Machine Learning; The What, Where and How of Data for Data Science; Data Lake the evolution of data processing
When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.
Learn how to find value and insight in outliers in the latest anomaly detection guidebook by Dataiku, which includes use cases, and step-by-step guidance (including code samples) to starting an anomaly detection project.
Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code."
KDnuggets poll compares Machine Learning Engineer, Researcher, Data Scientist and other professions and identifies one with the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job.
Statistics encourages us to think systemically and recognize that variables normally do not operate in isolation, and that an effect usually has multiple causes. Some call this multivariate thinking. Statistics is particularly useful for uncovering the Why.
With Anaconda Enterprise, National Grid was able to implement a more informed and cost-effective system that allowed for greater accuracy in modeling and predicting maintenance needs. Read the case study to learn more.
Apply online to The Institute for Statistics Education, the pioneer in online data science education. You can begin your online certificate right now - we offer rolling admission and introductory classes every month. Get started today!
Also: Introduction to Game Theory (Part 1); Human Interpretable Machine Learning (Part 1) - The Need and Importance of Model Interpretation; DIY Deep Learning Projects; 10 More Free Must-Read Books for Machine Learning and Data Science
This tutorial will explain the steps required to package your Python projects, distribute them in distribution formats using steptools, upload them into the Python Package Index (PyPI) repository using twine, and finally installation using Python installers such as pip and conda.
See some of data’s most fascinating people, from data’s most successful companies, talking about data’s most intriguing problems, at Strata Data Conference in New York, Sep 11-13. Save an additional 20% with code KDNU.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
Also: Who Is Going To Make Money In #AI?; Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch); Learning from Imbalanced Classes; 10 More Free Must-Read Books for Machine Learning and Data Science
Do your data visualizations need a reboot? Though data visualizations may be designed to facilitate understanding, not all graphs are effective. In this webcast, viewers will learn how to use best practices to give a graph a makeover.
We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
Move your career forward in one of the fields with the largest demand. Business Analytics at Clark University will give you the skills employers demand by teaching you how to synthesize data into powerful information.
For Carlos Carcach, Professor & Director, Center for Public Policy at the Escuela Superior de Economía y Negocios (ESEN) in Santa Tecla, El Salvador, gangs are an object of intellectual curiosity and the subject of his research.
NYU Stern MS in Business Analytics provides experienced professionals with a unique and valuable data-driven business perspective. This 1 year, part-time program is divided into 5 onsite modules with online independent study in between. Apply now.
Are you using your customer data to its full advantage? Chances are the answer is no. Customer Analytics from Wharton Executive Education, Sep. 17–21, 2018, Philadelphia, gives you a deeper, actionable understanding of your data.
The Big Data Toronto conference and expo is back for its 3rd edition on Jun 12-13, 2018 at the Metro Toronto Convention Centre. Big Data focuses on the skills, software and leadership needed to implement data insights & AI Toronto is dedicated to Toronto’s growing AI and deep learning communities.
Also: Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM; On the contribution of neural networks and word embeddings in NLP; Improving the Performance of a Neural Network; Python eats away at R
Learn how an analytics leader enables results with priorities that include evangelizing the importance of data-driven decision-making; aligning analytics with a business value-driven approach; and developing an analytics competency to train and develop staff.
Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.