Occam's Razor
MARCH 30, 2017
A rare post today. It looks a little further out into the future than I normally tend to. It attempts to simplify a topic that has more than it’s share of coolness, confusion and complexity. While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016.
Bruno Aziza
MARCH 22, 2017
Products do not often exhibit long-term disruptive value. Platforms do. From Snap to Amazon, companies that invested in underlying platform technology were able to exert lasting influence over the way in which people consume content and information.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Jim Harris
MARCH 15, 2017
Morte de Césare (Death of Caesar) by Vincenzo Camuccini , 1798 Today is the Ides of March (March 15), which back in 44 BC was definitely not a good day to be Julius Caesar, who was literally stabbed in the back by the Roman Senate during his assassination in the Theatre of Pompey (as depicted above), which was spearheaded by Brutus and Cassius in a failed attempt to restore the Roman Republic, but instead resulted in a series of civil wars that ultimately led to the establishment of the permanen
DSI Analytics
MARCH 6, 2017
Office Politics: A survivor’s guide for data scientists Everyone gets sunk by office politics at some point in their career, but data scientists are in some ways especially ill-prepared to navigate the unspoken rules and hidden agendas that together form a critical part of the corporate world. There are those who leverage office politics as […].
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
MLWhiz
MARCH 25, 2017
I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic. And it still doesn’t cease to amaze me.
TIBCO
MARCH 13, 2017
Little can rival the ascendancy of Artificial Intelligence’s (AI) profile from a niche concern to technology’s hottest theme, transcending the pages of the technical press to sit at the heart of mainstream culture. A certain breed of robotics has been a dominant force in this traction bringing machine learning to the masses in the form of chatbots and avatars that feature in our homes and customer service experience, as well as in banking and call centers.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Bruno Aziza
MARCH 17, 2017
You do not build a product in a vacuum. We build platforms with highly cross-functional teams that include people from partnerships, policy, support, engineering, product marketing, analytics, design, research, and many other groups.
Nutanix
MARCH 22, 2017
This morning on May 24th, 2016, we announced Nutanix Xpress – our first product line dedicated just to small and medium-sized businesses.
The Unofficial Google Data Science Blog
MARCH 13, 2017
By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs.
MLWhiz
MARCH 22, 2017
Today we will look into the basics of linear regression. Here we go : Contents Simple Linear Regression (SLR) Multiple Linear Regression (MLR) Assumptions 1. Simple Linear Regression Regression is the process of building a relationship between a dependent variable and set of independent variables. Linear Regression restricts this relationship to be linear in terms of coefficients.
Speaker: David Bard, Principal at VP Product Coaching
In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.
Bruno Aziza
MARCH 16, 2017
A new White House administration and Congress are moving quickly on high-profile policy like immigration and health care – issues that are dominating the headlines. Another potential policy shift that is getting much less mainstream media attention could threaten the privacy of nearly every American.
Bruno Aziza
MARCH 24, 2017
With a broad range of practical applications and rapidly evolving technology, drones offer huge untapped potential, but not every market offers equal opportunities for growth. Here are seven facts and forecasts to know before investing.
Bruno Aziza
MARCH 20, 2017
MongoDB CEO Dev Ittycheria explains how expensive poor hiring decisions are and why the devil you know may be better than the devil you don’t.
MLWhiz
MARCH 4, 2017
A data scientist needs to be Critical and always on a lookout of something that misses others. So here are some advices that one can include in day to day data science work to be better at their work: 1. Beware of the Clean Data Syndrome You need to ask yourself questions even before you start working on the data. Does this data make sense? Falsely assuming that the data is clean could lead you towards wrong Hypotheses.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
Bruno Aziza
MARCH 23, 2017
7 questions all business and IT leaders should be asking.
Nutanix
MARCH 15, 2017
There’s no question that enterprise application usability has for the most part been left behind.
Expert insights. Personalized for you.
166 
Let's personalize your content