Eliseo  Wolf

Eliseo Wolf

1583387880

Get Started With Netlify CMS (with Benaiah Mischenko) — Learn With Jason

Netlify CMS is a way for developers to manage site content through Git, but with a UI. And with new support for open authoring in GitHub, getting a community contribution is more approachable than you might think. Benaiah Mischenko (https://twitter.com/BMischenko) teaches us how to get started with Netlify CMS and walks us through setting up Open Authoring.

#netlify #developer

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Buddha Community

Get Started With Netlify CMS (with Benaiah Mischenko) — Learn With Jason
Eliseo  Wolf

Eliseo Wolf

1583387880

Get Started With Netlify CMS (with Benaiah Mischenko) — Learn With Jason

Netlify CMS is a way for developers to manage site content through Git, but with a UI. And with new support for open authoring in GitHub, getting a community contribution is more approachable than you might think. Benaiah Mischenko (https://twitter.com/BMischenko) teaches us how to get started with Netlify CMS and walks us through setting up Open Authoring.

#netlify #developer

Obie  Rowe

Obie Rowe

1598403060

How To Get Started With Machine Learning With The Right Mindset

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.

Before we dive into the machine learning world, you should take a step back and think, what is stopping you from getting started? If you think about it, most of the time, we presuppose things about ourselves and assume that to be true without question.

The most normal presumption that we make about ourselves is that we need to have prior knowledge before getting started. Get a degree, complete a course, or have a good understanding of a particular subject.

The truth is that most of the time, this is a lie, the prior knowledge you think you need is most of the time not required or is so big that even experts from the field don’t fully understand it. The Seek of this prior knowledge is a trap that will make you run in circles, which leads us to the next presumption.

The perfect condition, you can’t wait for the ideal environment or situation to get started, things will never be 100% ready, try and fail, then try again. It takes a lot of time to get good at machine learning; you won’t learn all at once and especially at the beginning.

Instead of trying to acknowledge everything before getting started, do a little bit every day; you can make significant progress by creating small things every day for a considerable amount of time. The perfect condition will never exist, do it in your path, be consistent with it, and the results will come.

After you start making little progress every day, you probably will end up having a struggle with something or failing to achieve your goal at a certain point. This feeling is tough; it’s hard to see yourself not making any progress, not having any sense of gratification, and then still not give up.

Machine learning is hard, it might take you a few weeks, months or even years to see progress in a certain point but isn’t any harder than any other technical skill, it requires repetition and dedication to get where you want, you need to test it, make a mistake and learn from i

#machine-learning #artificial-intelligence #python-machine-learning #learn-machine-learning #latest-tech-stories #machine-learning-uses #ml-top-story #ai-and-ml

Why you should learn Computer Vision and how you can get started

I. Motivation

In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?

II. What is Computer Vision?

Image for post

Figure 1: Portrait of Larry Roberts.
The field of Computer Vision dates back to the 1960s when Larry Roberts, who is now widely considered as the “Father of Computer Vision”, published his paper _Machine Perception of Three-Dimensional Solids _detailing how a computer can infer 3D shapes from a 2D image (Roberts, 1995). Since then, other researchers have made amazing contributions to the field. These advances, however, have not changed the underlaying goal of Computer Vision which is to mimic the human visual system. From an engineering point of view, this means being able to build autonomous systems that can do things a human visual system can do such as detecting and recognizing objects, recognizing faces and facial expressions, etc. (Huang, 1996). Traditionally, many approaches in Computer Vision involves manual feature extraction. This means manually finding some unique features/characteristics (edges, shapes, etc) that are only present in an object to be able to detect and recognize what that object is. Unfortunately, one major issue arises when trying to detect and recognize variations (sizes, lightning conditions, etc) of that same object. It is difficult to find features that can uniquely identify an object across all variations. Fortunately, this problem is now solved with the introduction of Machine Learning, particularly a sub-field of Machine Learning called Deep Learning. Deep Learning utilizes a form of Neural Networks called Convolutional Neural Networks (CNNs). Unlike the traditional methods, methods that utilize CNNs are able to extract features automatically. Instead of trying to figure out which features can represent an object manually, a CNN can learn those features automatically by looking at many variations of that same object. As result, many recent advancements in the field of Computer Vision involves the use of CNNs.

#computer-science #machine-learning #deep-learning #computer-vision #learning #deep learning

Matteo  Renner

Matteo Renner

1617792300

The Most Important Programming Lesson I Ever Learned

In the fall of 2012, I walked into my graduate advisor’s office and asked her which computer science class she recommended for me to enroll in. I explained that I was a complete novice in programming. She suggested Introduction to C Programming.

After attending a few lectures, I discover that the majority of the students I spoke to in this introductorycourse had some prior experience in programming.

Six weeks and 80 hours of work later, I dropped the course.

Enter spring semester of 2013. I enrolled in an easier computer science course, Introduction to Computer Programming via the Web. I breezed through the first quarter of the course, executing HTML and CSS with ease. Then, we started Javascript (JS). That feeling of constant anxiety and stress from my previous computer science course returned in full fashion. It was too late in the semester to drop the course, so I asked a friend for help.

#debugging #learning-to-code #learning-to-program #computer-science-basics #how-to-start-learning-to-code #python-programming #learn-javascript #learn-python #web-monetization

Ray  Patel

Ray Patel

1625843760

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services