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.
Accelerate gives data visionaries like you expert guidance and insight to further your business and career goals, in just three days. Super Early Bird till Aug 25 - save 20% with code ACCKD01.
We consider scraping data from online food blogs to construct a data set of recipes with ingredients, nutritional information and more, and do exploratory analysis which provides tasty insights.
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.
Large scale simulation of random number generation is possible with today’s high speed & scalable distributed computing frameworks. Let’s understand how it can be achieved using Apache Spark.
In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.
Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
TDWI Anaheim is the leading event for Analytics, Big Data, and Data Science Training. Use code KD30 to save 30% through July 14 and check out our amazing speaker lineup.
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.
Interesting finding include: salaries for early career data scientists decrease for the first time in four years, percent of early career data scientists with a PhD drops - read more for details.
Spark has been useful in mapping out genetic traits that can be associated with certain diseases and the genetic makeup of microorganisms that live in our bodies.
dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp
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.
Also 10 Free Must-Read Books for #MachineLearning and #DataScience; #Keras implementation of a simple Neural Net module for relational reasoning; Applying #deeplearning to real-world problems
We examine which top tools are "friends", their Python vs R bias, and which work well with Spark/Hadoop and Deep Learning, and identify an emerging Big Data Deep Learning ecosystem.
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.
As businesses grow, the tools and technologies they rely on must either evolve with them, or be replaced. Tools that worked for a team of 10 may no longer for a team of 50 or more.
This new course with limited places will focus on AI design (product, development and Data) for the fintech industry and will be taught online by Ajit Jaokar and Jakob Aungiers.
Swarm Intelligence is using many simple machine learning models good at one small task to solve bigger, more complex problems. We examine how it can improve sentiment analysis and measuring emotions.
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.
Here are some of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until June 21, 2017.
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
You can be recognised for your skills in data analytics in just six weeks by IAPA. Act before 30 June and claim the cost of the IAPA-certified via credential as a tax deduction.
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.
In this article we will focus — basic deep learning using Keras and Theano. We will do 2 examples one using keras for basic predictive analytics and other a simple example of image analysis using VGG.
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.
DataScience.com and RStudio are co-hosting a free webinar on June 15 to showcase how RStudio’s suite of tools for R seamlessly integrate with the DataScience.com Platform.
Come see top experts and practitioners present at Predictive Analytics World for Financial this October 29-November 2 in New York City. Minimize risk and multiply returns with data science!
Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.
Learn from early-adopting marketing practitioners, AI technology developers, and industry analysts with their fingers on the pulse of this developing technology. Save with code KDN15.
Download this chapter by Gordon Linoff and Michael Berry, and learn how to create derived variables, which allow the statistical modeling process to incorporate human insights.
You know how much value and insight Predictive Analytics World offers and we want you to be among the first to know what’s on tap October 29-November 2, 2017 in New York City.
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.
In this approach, problem dataset and its Neural network are specified in a PMML like XML file. Then it is used to populate the TensorFlow graph, which, in turn run to get the results.
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?
Penn State World Campus offers a 9-credit Business Analytics Graduate Certificate and 30-credit online Master's Degree in Data Analytics - Business Analytics Option. Register now to start in August.
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.
The second post in this series of tutorials for implementing machine learning workflows in Python from scratch covers implementing the k-means clustering algorithm.
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.
Even as cyber criminals and swindlers step up their game, companies can use predictive analytics to stay ahead. Discover the full scope of IBM SPSS predictive analytics capabilities.
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.
This one-year, part-time program is divided into five onsite modules: three in NYC and two in rotating global locations. Our first application deadline for the new incoming class is August 1, 2017.
Predictive Analytics World for Business & Predictive Analytics World for Financial Services come to New York, Oct. 29 - Nov. 2. Register now at Super Early Bird rates!
We interview leading women in STEM to learn more about how we can all work to make science and technology industries more inclusive. How can more women be encouraged to work in these fields?
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?
Ontotext live, online training designed to improve understanding of how Semantic Technology operates to help you make best use of it. Sign up by June 12 to save.
Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop.
Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.
RE•WORK's Machine Intelligence Summit and Machine Intelligence In Autonomous Vehicles Summit take place June 28-29 in Amsterdam. Save 20% with code KDNUGGETS.
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.
Coming soon: Spark Summit San Francisco, Open Data London, PAW Chicago, Big Data Toronto, O'Reilly AI NYC, Sentiment Symposium NYC, Postgres Vision Boston, and many 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.
Applying Deep Learning to Real-world Problems
Text Clustering: Get quick insights from Unstructured Data
The Machine Learning Algorithms Used in Self-Driving Cars
Top 15 Python Libraries for Data Science in 2017
6 Interesting Things You Can Do with Python on Facebook Data
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