High-quality teaching resources for computer science teachers, to reignite your passion for teaching computing from nicholawilkin.com.
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The article quoted below by Florence R. Sullivan & Jill Denner calls for us to go beyond "simply giving more students access." We need to give them "functional computer science literacy." By that phrase, they mean that we need to have students consider ethical and social issues. That's not what Andy DiSessa meant when he…
This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.
There's a saw for that. Different saws are suited for different tasks. Here's a basic reference guide for 10 types of saws and their uses.
One-Pagers — JAMIE CLARK
Choose your students' English level: News update April 8th: I finally finished writing up my series of posts about Computer Science with First Grade. Read about our final presentations, and play my students' games here! Part 1, part 2, part 3, part 4 and part 5 are still up if you missed them. Sessions…
Animation, code, analysis, and discussion of 8 sorting algorithms on 4 initial conditions.
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Eben Upton is best known as the man behind the Raspberry Pi, a tiny, $25 computer designed to help turn kids into programmers. Upton priced it at $25 because he thought that's around what an average textbook cost: "I now understand that's an incorrect estimate. If we had a better idea of what school textbooks cost we would have had an easier job with the engineering over the years," he joked to Wired years later. It's a funny story but also a sad story. Textbooks are expensive. More expensive than most non-students even realize.
Seguridad es la cuarta área dentro de la Competencia Digital Docente y cubre el uso responsable y seguro de la tecnología, la protección de datos, de información y de la identidad digital. La tecno…
As with the other videos from our codecentric.ai Bootcamp (Random Forests, Neural Nets & Gradient Boosting), I am again sharing an English version of the script (plus R code) for this most recent addition on How Convolutional Neural Nets work. In this lesson, I am going to explain how computers learn to see; meaning, how do they learn to recognize images or object on images? One of the most commonly used approaches to teach computers “vision” are Convolutional Neural Nets. This lesson builds on top of two other lessons: Computer Vision Basics and Neural Nets. In the first video, Oli explains what computer vision is, how images are read by computers and how they can be analyzed with traditional approaches, like Histograms of Oriented Gradients and more. He also shows a very cool project, that he and colleagues worked on, where they programmed a small drone to recognize and avoid obstacles, like people. This video is only available in German, though. In the Neural Nets blog post, I show how Neural Nets work by explaining what Multi-Layer Perceptrons (MLPs) are and how they learn, using techniques like gradient descent, backpropagation, loss and activation functions. Convolutional Neural Nets Convolutional Neural Nets are usually abbreviated either CNNs or ConvNets. They are a specific type of neural network that has very particular differences compared to MLPs. Basically, you can think of CNNs as working similarly to the receptive fields of photoreceptors in the human eye. Receptive fields in our eyes are small connected areas on the retina where groups of many photo-receptors stimulate much fewer ganglion cells. Thus, each ganglion cell can be stimulated by a large number of receptors, so that a complex input is condensed into a compressed output before it is further processed in the brain. How does a computer see images Before we dive deeper into CNNs, I briefly want to recap how images can take on a numerical format. We need a numerical representation of our image because just like any other machine learning model or neural net, CNNs need data in form of numbers in order to learn! With images, these numbers are pixel values; when we have a grey-scale image, these values represent a range of “greyness” from 0 (black) to 255 (white). Here is an example image from the fruits datasets, which is used in the practical example for this lesson. In general, data can be represented in different formats, e.g. as vectors, tables or matrices. I am using the imager package to read the image and have a look at the pixel values, which are represented as a matrix with the dimensions image width x image height. library(imager) im
hi-level webpage about software & data artifacts in the ACM DL
In our ongoing Game Engines by Language series, today we are looking at the languages available for Python developers. That is, game engines where you can s...
Here's why the relationship between content and sales is so important.
From blended learning to computerized testing, digital and online technologies are reshaping the classroom experience for students.
This lesson / resource covers the Budget Variances . It designed to follow the lesson covering Introductory concepts of Budgets for Business. This lesson has been designed for AP / A Level and IB Diploma Business Studies. To see this lesson in a full scope and sequence / scheme of work, please clic...
Connecting littleBits to Scratch 2.0 using the littleBits Arduino bit and a Scratch 2.0 extension.Need to have:- littleBits http://littlebits.cc- littleBits...
The PocketQube team wants to give even more people access to its relatively affordable 5cm x 5cm x 5cm microsatellites, so it now has ready-to-use kits up for sale on its online store. These kits contain (almost) everything one needs to assemble cube satellites, making it easier for first-timers or hobbyists with no advisers to guide them when building their first structure. Each bundle contains the cube's skeleton, a radio board for communication, a Labsat board that tests electronics included in the setup, and an onboard computer to serve as the central command station. They're available in one-, two- and three-cube configurations for $5,999, $6,149 and $6,299, respectively.
