The Lean Startup provides a scientific approach to creating and managing startups and get a desired product to customers’ hands faster. The Lean Startup method teaches you how to drive a startup-how to steer, when to turn, and when to persevere-and grow a business with maximum acceleration. It is a principled approach to new product development. Too many startups begin with an idea for a product that they think people want. They then spend months, sometimes years, perfecting that product without ever showing the product, even in a very rudimentary form, to the prospective customer. When they fail to reach broad uptake from customers, it is often because they never spoke to prospective customers and determined whether or not the product was interesting. When customers ultimately communicate, through their indifference, that they don’t care about the idea, the startup fails.
1. Entrepreneurs Are Everywhere
2. Entrepreneurship Is Management
3. Validated Learning
4. Innovation Accounting
1. Entrepreneurs Are Everywhere
2. Entrepreneurship Is Management
3. Validated Learning
4. Innovation Accounting
Before we get into it, let’s look at how Excel’s VLOOKUP function works so it is clear what we’re reproducing in R. VLOOKUP is used to copy data from one dataset to another based on matching values. ‘Dataset’ in this case can refer to a column, table, sheet, etc. For example, you may have one sheet that has contact info for your customers. From your email program, you have a list of email addresses with an action taken on an email campaign. Now you want to combine your contact data with your email open/click data. With VLOOKUP, you can match by the ’email’ column in each dataset and copy over the open/click data to the contact data.
If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Python – usability, popularity index, advantages & limitations, job opportunities, and salaries.
Depending on what news headline you have read, you may have perceived an Artificial Intelligence (AI) system as either an Alexa or Siri assistant that understands all your commands, a deep learning system that can recognize dog or a cat from image, a system that recommends personalized medicine, or an intelligent, overpowering machine that can overtake all human tasks and render humans useless. Few of these definitions can be termed as visionary, few fear mongering and rest of them being evolutionary.
Convolutional neural network – In this article, we will explore our intuitive explanation of convolutional neural networks (CNN’s) on high level. CNN’s are inspired by the structure of the brain but our focus will not be on neural science in here as we do not specialise in any biological aspect. We are going artificial in this post.
With the use of tidyverse package is become easy to manage and create new datasets. Among many other useful functions that tidyverse has, such as mutate or summarise, other functions including spread, gather, separate, and unite are less used in data management. Therefore, in this post, I will focus on those functions. I will show how to transform the dataset from long to wide, how to separate one variable in two new variables or to unite two variables into one.
Often there’s a need to abstract away your machine learning model details and just deploy or integrate it with easy to use API endpoints. For eg., We can provide a URL endpoint using which anyone can make a POST request and they would get a JSON response of what the model has inferred without having to worry about its technicalities. In this tutorial, we will create a TensorFlow Serving server to deploy our InceptionV3 image classification convolutional neural network (CNN) built in Keras. We will then create a simple Flask server which will accept POST request and do some image preprocessing, required for Tensorflow serving server, and return a JSON response.
Frameworks such as Tensorflow, Pytorch, Theano and Cognitive Toolkit (CNTK) (and by extension any deep learning library which works alongside them, e.g. Keras) permit significantly faster training of deep learning when they are set up with GPU (graphics processing unit) support compared withusing a CPU. However, for GPU support to be available for those frameworks, the GPU itself must be compatible with the CUDA toolkit and any additional required GPU-Accelerated Libraries, for example cuDNN. At present, CUDA compatibility is limited to Nvidia GPUs. So, if you have other GPU hardware (e.g. AMD), you will have to swap it out of your computer and set up new driver software. This is what I had to do for my old (ish) PC, and this post guides you through that process, step by step.
If you tried to answer the question in the title, you’ll be disappointed to find out that it is actually a trick question – there is essentially no difference in the listed terms. Just like the issue mentioned in ANCOVA and Moderation, different terms are often used for the same thing, especially when they belong to different fields. This post will attempt to dispel the confusion by bringing these terms together, and explain how to interpret the cells of a confusion matrix using the context of detecting an effect.
Human intelligence is not just about the brain, education is an essential part of our intelligence too and we can improve human intelligence with better education. But it seems like we are much more successful in training machines than we are in training humans. There might be many possible explanations for this. AI is a mathematical construct and most of the time we can craft a performance metric to define better whereas education has economic, social, political and religious components and the definition of better becomes subjective. In addition, in AI, we can experiment much more freely to find out which learning method works best. On the other hand, there are many limitations (finances, time etc.) to experimentation in the field of education. Finally, there are benchmark datasets which help people around to world compare their machine learning methods. Such a universal comparison is very hard to achieve for education.
You love baking and several of your friends have complimented your delicious cupcakes. Lately, you’ve been tinkering with a few ideas that would take your pumpkin spice cupcakes to a whole new level. But you’re not sure which of the improvements you have in mind will be successful or not among your friends. Thankfully, you’ve heard about A/B Testing. In A/B Testing you run two randomized experiments, that test two variants A and B, also referred to as control and experiment. This technique iswidely used to fine-tune customer experience in mobile and web applications, since it helps making business decisions based on how users interact with a product.
In this article, we are going to examine Telosys, a code generation tool. We will do that by talking with its author, Laurent Guerin. This will give us the possibility to learn also about his views on code generation and what its users have accomplished by using Telosys.
TimelineJS 3 is an open source storytelling tool that anyone can use to create visually rich, interactive timelines to post on their websites. To get started, simply click ‘Make a Timeline’ on the homepage and follow the easy step-by-step instructions. TimelineJS was developed at Northwestern University’s KnightLab in Evanston, Illinois. KnightLab is a community of designers, developers, students, and educators who work on experiments designed to push journalism into new spaces. TimelineJS has been used by more than 250,000 people, according to its website, to tell stories viewed millions of times. And TimelineJS3 is available in more than 60 languages.
Having a solid grasp on deep learning techniques feels like acquiring a super power these days. From classifying images and translating languages to building a self-driving car, all these tasks are being driven by computers rather than manual human effort. Deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis. Understandably, a ton of folks are suddenly interested in getting into this field. But where should you start? What should you learn? What are the core concepts that actually make up this complex yet intriguing field? I’m excited to pen down a series of articles where I will break down the basic components that every deep learning enthusiast should know thoroughly. My inspiration comes from deeplearning.ai, who released an awesome deep learning specialization course which I have found immensely helpful in my learning journey. In this article, I will be writing about Course 1 of the specialization, where the great Andrew Ng explains the basics of Neural Networks and how to implement them. Let’s get started!
As the healthcare system moves toward value-based care, CMS has created many programs to improve the quality of care of patients. One of these programs is called the Hospital Readmission Reduction Program (HRRP), which reduces reimbursement to hospitals with above average readmissions. For those hospitals which are currently penalized under this program, one solution is to create interventions to provide additional assistance to patients with increased risk of readmission. But how do we identify these patients? We can use predictive modeling from data science to help prioritize patients. One patient population that is at increased risk of hospitalization and readmission is that of diabetes. Diabetes is a medical condition that affects approximately 1 in 10 patients in the United States. According to Ostling et al, patients with diabetes have almost double the chance of being hospitalized than the general population (Ostling et al 2017). Therefore, in this article, I will focus on predicting hospital readmission for patients with diabetes.
In this post, we will develop an intuitive sense for an important concept in Machine Learning called the Bias-Variance Tradeoff. Before we dive into the subject, allow me to go off on a tangent about human learning for a little bit. Practice alone does not make you better at a skill. We all know people who practice very hard but never seem to accomplish much. The reason is that they do not direct their effort appropriately. For a newbie who is learning the Piano, it is tempting to play the tune she has mastered, over and over again because it feels comfortable and it provides a lot of joy and sense of accomplishment. This behavior, though, does not help her improve her skill. The right way to practice is to identify your weakest areas and direct a massive effort on improving those areas without worrying about areas in which you are already good. Psychologists call this Deliberate Practice. This form of practice is not very enjoyable. It is slow, frustrating and arduous. But Deliberate Practice is extremely effective in improving performance.