With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.
Did you ever learn something you didn't really want to? The path to machine learning mastery is paved with such collateral knowledge. Here are a few examples of such information I have gleaned while trekking away.
Grouping and clustering free text is an important advance towards making good use of it. We present an algorithm for unsupervised text clustering approach that enables business to programmatically bin this data.
This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.
IBM, a leader in 2017 Forrester Wave Report for Predictive Analytics and Machine Learning Solutions, offers data scientists a complete toolkit, including predictive analytics and machine learning capabilities and more.
The leading vendor-neutral conference about predictive analytics is holding its seventh annual conference this October 11-12. Once again it's time for all predictive analytics smartest minds to gather and explore all the latest.
Emerging Ecosystem: Data Science and Machine Learning Software, Analyzed; The Machine Learning Algorithms Used in Self-Driving Cars; The world’s first protein database for Machine Learning and AI; Making Sense of Machine Learning; 75 Big Data Terms to Know to Make your Dad Proud
Gain some insight on a variety of topics with select answers from Quora's current top machine learning writers. Advice on research, interviews, hot topics in the field, how to best progress in your learning, and more are all covered herein.
This post discusses a variety of contemporary Deep Meta Learning methods, in which meta-data is manipulated to generate simulated architectures. Current meta-learning capabilities involve either support for search for architectures or networks inside networks.
This post outlines a data analysis exercise undertaken by students in a recent University of San Francisco MBA class, in which they were forced to make difficult data science trade-offs between gathering data, preparing the data and performing the actual analysis.
Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.
In businesses everywhere, the digital transformation is spawning a bunch of new job titles. Among them are Chief Data Officer, Big Data Architect and Data Visualizer. All these sought-after specialist data roles are having a major impact on the workplace.
In the past, ML learning hasn't had as much success in cyber security as in other fields. Many early attempts struggled with problems such as generating too many false positives, which resulted mixed attitudes towards it.
Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.
Top 15 Python Libraries for Data Science in 2017; Deep Learning Papers Reading Roadmap; The Practical Importance of Feature Selection; Understanding Deep Learning Requires Re-thinking Generalization; K-means Clustering with Tableau
Data ScienceTech Institute is the 1st private postgraduate school in pure Data Science & Big Data education in France! Data ScienceTech Institute's mission is simple: training executive students to become ready-to-go Read more »
Chief Analytics Officer, Oct 2-5 in Boston, will be the largest, most senior gathering of analytics leaders in North America, providing a platform for over 300+ attendees and 125+ speakers to share best practice and explore strategies for driving actionable insights through analytics. Special KDnuggets offer - book by June 23.
We show how to use Tableau 10 clustering feature to create statistically-based segments that provide insights about similarities in different groups and performance of the groups when compared to each other.
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
Powered by Apache Spark, Databricks provides an end-to-end platform designed to help data engineers and data scientists easily implement advanced analytics at scale. Download the Making Machine Learning Simple Whitepaper from Databricks to learn more.
The reason we have pseudorandom numbers is because generating true random numbers using a computer is difficult. Computers, by design, are excellent at taking a set of instructions and carrying them out in the exact same way, every single time.
Machine Learning in Real Life: Tales from the Trenches; Is Regression Analysis Really Machine Learning?; Implementing Your Own k-Nearest Neighbour Algorithm Using Python; Building Simple Neural Networks - TensorFlow for Hackers.
Successful analytics at the organizational-level starts with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Seattle in July, Live online in September, and Wash-DC in October.
Recently, PSL Research University launched a one-week course combining theoretical lectures and practical sessions. 115 students from various backgrounds and skill levels were enrolled; something quite spectacular happened during the week: Students have achieved an astounding level of score improvement - in just three afternoons.
Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
Michael Milford, Associate Professor at Queensland University of Technology (QUT), is a leading robotics researcher working to improve perception and more in autonomous vehicles, conducting his research at the intersection of robotics, neuroscience and computer vision.
Is Regression Analysis Really Machine Learning?; 6 Interesting Things You Can Do with Python on Facebook Data; A Practical Guide to Machine Learning; K-means Clustering with R: Call Detail Record Analysis; Machine Learning in Real Life: Tales from the Trenches to the Cloud
With the RStudio integration, DataScience.com customers are able to write and run code in RStudio while benefitting from additional features of the platform: on-demand infrastructure, pre-configured environments, secret management, and more.
Deep Image Analogy; Example-Based Synthesis of Stylized Facial Animations; Google releases dataset of 50M vector drawings, open sources Sketch-RNN implementation; New massive medical image dataset coming from Stanford; Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?
Explore the cutting-edge technology leading the way in Machine Intelligence and Autonomous Vehicles and it’s applications in industry at the Amsterdam Summits on June 28th & 29th. Use the discount code KDNUGGETS to save 20% on all tickets.
As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!
We live in a world where everyone knows enough about the Buzzwords “Deep Learning” and “Big Data”... we also live in a world where if you’re a developer you can, while knowing nothing about machine learning, go from zero to training a OCR model in the space of an hour.
The Artificial #ArtificialIntelligence Bubble and the Future of #Cybersecurity; Which #MachineLearning #Algorithm Should I Use? A handy #cheatsheet; 50 Companies Leading The #AI Revolution, Detailed; #MachineLearning Workflows in #Python from Scratch Part 1: Data Preparation
For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you're working on your design-to-production pipeline.
Data sciences can also be used by HR manager to create several estimates like the investment on talent pool, cost per hire, cost on training, and cost per employee. It provides better techniques for optimization, forecasting, and reporting.
In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors.
Facebook has a huge amount of data that is available for you to explore, you can do many things with this data. I will be sharing my experience with you on how you can use the Facebook Graph API for analysis with Python.
Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics.
Over the next couple months, we’re going to challenge you to apply TPOT to any data science problem you find interesting on Kaggle. If your entry ranks in the top 25% of the leaderboard on a Kaggle problem, we want to see how TPOT helped you accomplish that.
Machine Learning Workflows in Python from Scratch Part 1: Data Preparation; Which Machine Learning Algorithm Should I Use?; 7 Steps to Mastering Data Preparation with Python; 7 Techniques to Handle Imbalanced Data; Why Does Deep Learning Not Have a Local Minimum?
What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?
A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.
What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.