Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.
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.
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
Gaussian distribution model, often identified with its iconic bell shaped curve, also referred as Normal distribution, is so popular mainly because of three reasons.
Machine Learning Services in SQL Server eliminates the need for data movement - you can install and run R/Python packages to build Deep Learning and AI applications on data in SQL Server.
Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
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.
Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.
Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
We all know correlation doesn’t equal causality at this point, but when working with time series data, correlation can lead you to come to the wrong conclusion.
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.
In honor of 2018 FIFA World Cup in Football, we update our classic KDnuggets cartoon - what players can do when their moves are predicted by Machine Learning?
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.
While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.
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.
After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.
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.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
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.
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.
Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.
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.
The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them.
Check out this game theory basics post for an introduction to Two-player Sequential games — Dominant Strategies, Nash Equilibrium, and Cooperation vs. Defection.
The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem.
A comprehensive list of resources for Women in Data Science and Machine Learning, including a list of useful tech groups and published lists for finding Women speakers.
In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.
In this webinar on Jun 5, 1:00 pm ET, analytics industry expert Jen Underwood will demonstrate how to visualize machine learning results with dashboard tools.
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.
In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.
Top 20 Python Libraries for Data Science in 2018
5 Data Science Projects That Will Get You Hired in 2018
Detecting Sarcasm with Deep Convolutional Neural Networks
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