What Is 66DaysOfCAE and How Can You Join The Engineering Community and Start Learning.

What is 66DaysOfCAE and how can you join the engineering community and start learning. This is a great opportunity if you have ever struggled to complete a course or project. By joining the community you can share you progress and get feedback from like minded people while creating good habits.

  • 00:00 Intro
  • 00:23 What is 66DaysOfCAE
  • 01:30 What is CAE
  • 02:18 Benefits of joining
  • 03:27 What will I do?
  • 03:45 Who can join?

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What Is 66DaysOfCAE and How Can You Join The Engineering Community and Start Learning.

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?

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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

Brad  Hintz

Brad Hintz

1599302760

Apache Spark’s Join Algorithms

One of the most frequently used transformations in Apache Spark is Join operation. Joins in Apache Spark allow the developer to combine two or more data frames based on certain (sortable) keys. The syntax for writing a join operation is simple but some times what goes on behind the curtain is lost. Internally, for Joins Apache Spark proposes a couple of Algorithms and then chooses one of them. Not knowing what these internal algorithms are, and which one does spark choose might make a simple Join operation expensive.

While opting for a Join Algorithm, Spark looks at the size of the data frames involved. It considers the Join type and condition specified, and hint (if any) to finally decide upon the algorithm to use. In most of the cases, Sort Merge join and Shuffle Hash join are the two major power horses that drive the Spark SQL joins. But if spark finds the size of one of the data frames less than a certain threshold, Spark puts up Broadcast Join as it’s top contender.

Broadcast Hash Join

Looking at the Physical plan of a Join operation, a Broadcast Hash Join in Spark looks like this

Joins in Apache Spark: Broadcast Join

The above plan shows that the data frame from one of the branches broadcasts to every node containing the other data frame. In each node, Spark then performs the final Join operation. This is Spark’s per-node communication strategy.

Spark uses the Broadcast Hash Join when one of the data frame’s size is less than the threshold set in spark.sql.autoBroadcastJoinThreshold. It’s default value is 10 Mb, but can be changed using the following code

spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 100 * 1024 * 1024)

This algorithm has the advantage that the other side of the join doesn’t require any shuffle. If this other side is very large, not doing the shuffle will bring notable speed-up as compared to other algorithms that would have to do the shuffle.

Broadcasting large datasets can also lead to timeout errors. A configuration spark.sql.broadcastTimeout sets the maximum time that a broadcast operation should take, past which the operation fails. The default timeout value is 5 minutes, but it can be set as follows:

spark.conf.set("spark.sql.broadcastTimeout", time_in_sec)

Sort Merge Join

If neither of the data frames can be broadcasted, then Spark resorts to Sort Merge Join. This algorithm uses the node-node communication strategy, where Spark shuffles the data across the cluster.

Sort Merge Join requires both sides of the join to have correct partitioning and order. Generally, this is ensured by** shuffle and sort** in both branches of the join as depicted below

#apache spark #scala #tech blogs #broadcast join #join opertaions #join optimization #joins in spark #shuffled hash join #sort merge join

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Anastasia soda

Anastasia soda

1624309200

10 Highest Paying Jobs You Can Learn (Without College). ( HOT NEWS!!! )

These are the 10 highest paying jobs you can learn without needing a college degree. Jobs that pay $75,000 and higher.
📺 The video in this post was made by Andrei Jikh
The origin of the article: https://www.youtube.com/watch?v=QIGDA2JRz8w
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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