AI News, How should I prepare for an interview with the Amazon machine learning group?

How should I prepare for an interview with the Amazon machine learning group?

Since I have no experience on the technical side of this, I’ll give you the steps I would use if a client asked me to help him/her prepare for the interview.

First off, I would google Amazon Machine Learning to see what as a company, they are trying to do and why: Amazon Machine Learning - Predictive Analytics with AWS From there, I can see what updates they have done recently on the project, the type of customers they’ve had success with, and some of the marketing material.

If you have 2nd Level, 3rd Level or no direct connections, I would study their profiles and see their work history.

This is Why Making Strong Learning Connections Matters in Teaching

Making strong learning connections matters in all our teaching.

Simply put, it is to help others benefit from learning, namely those who’ll continue to develop our society.

When we are long gone, the quality of learning connections we give our students will influence their decisions for years to come.

Without connection there is no interest, and interest always precedes meaningful and authentic learning. So it’s essential that we are making strong learning connections to help them develop the thinking habits they need to succeed.

The reasoning behind this was to show how making strong learning connections is the start of any learning journey.

The line separating teaching from learning is between connection and remembering, and it’s where learning actually begins.

In making strong learning connections with activities that foster relevant skills with applicable value, we create higher-level thinking.

Therefore, by focusing on making strong learning connections first, we’ve provided learning of great value to our students.

One place to look for inspiration is our list of success stories featuring teachers from all over the world who helped their own students shine.

New Theory Cracks Open the Black Box of Deep Neural Networks

Even as machines known as “deep neural networks” have learned to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, they have also confounded their human creators, who never expected so-called “deep-learning” algorithms to work so well.

During deep learning, connections in the network are strengthened or weakened as needed to make the system better at sending signals from input data—the pixels of a photo of a dog, for instance—up through the layers to neurons associated with the right high-level concepts, such as “dog.” After a deep neural network has “learned” from thousands of sample dog photos, it can identify dogs in new photos as accurately as people can.

The magic leap from special cases to general concepts during learning gives deep neural networks their power, just as it underlies human reasoning, creativity and the other faculties collectively termed “intelligence.” Experts wonder what it is about deep learning that enables generalization—and to what extent brains apprehend reality in the same way.

Some researchers remain skeptical that the theory fully accounts for the success of deep learning, but Kyle Cranmer, a particle physicist at New York University who uses machine learning to analyze particle collisions at the Large Hadron Collider, said that as a general principle of learning, it “somehow smells right.” Geoffrey Hinton, a pioneer of deep learning who works at Google and the University of Toronto, emailed Tishby after watching his Berlin talk.

“I have to listen to it another 10,000 times to really understand it, but it’s very rare nowadays to hear a talk with a really original idea in it that may be the answer to a really major puzzle.” According to Tishby, who views the information bottleneck as a fundamental principle behind learning, whether you’re an algorithm, a housefly, a conscious being, or a physics calculation of emergent behavior, that long-awaited answer “is that the most important part of learning is actually forgetting.” Tishby began contemplating the information bottleneck around the time that other researchers were first mulling over deep neural networks, though neither concept had been named yet.

“For many years people thought information theory wasn’t the right way to think about relevance, starting with misconceptions that go all the way to Shannon himself.” Claude Shannon, the founder of information theory, in a sense liberated the study of information starting in the 1940s by allowing it to be considered in the abstract—as 1s and 0s with purely mathematical meaning.

Using information theory, he realized, “you can define ‘relevant’ in a precise sense.” Imagine X is a complex data set, like the pixels of a dog photo, and Y is a simpler variable represented by those data, like the word “dog.” You can capture all the “relevant” information in X about Y by compressing X as much as you can without losing the ability to predict Y.

“My only luck was that deep neural networks became so important.” Though the concept behind deep neural networks had been kicked around for decades, their performance in tasks like speech and image recognition only took off in the early 2010s, due to improved training regimens and more powerful computer processors.

The basic algorithm used in the majority of deep-learning procedures to tweak neural connections in response to data is called “stochastic gradient descent”: Each time the training data are fed into the network, a cascade of firing activity sweeps upward through the layers of artificial neurons.

When the signal reaches the top layer, the final firing pattern can be compared to the correct label for the image—1 or 0, “dog” or “no dog.” Any differences between this firing pattern and the correct pattern are “back-propagated” down the layers, meaning that, like a teacher correcting an exam, the algorithm strengthens or weakens each connection to make the network layer better at producing the correct output signal.

As a deep neural network tweaks its connections by stochastic gradient descent, at first the number of bits it stores about the input data stays roughly constant or increases slightly, as connections adjust to encode patterns in the input and the network gets good at fitting labels to it.

Questions about whether the bottleneck holds up for larger neural networks are partly addressed by Tishby and Shwartz-Ziv’s most recent experiments, not included in their preliminary paper, in which they train much larger, 330,000-connection-deep neural networks to recognize handwritten digits in the 60,000-image Modified National Institute of Standards and Technology database, a well-known benchmark for gauging the performance of deep-learning algorithms.

Brenden Lake, an assistant professor of psychology and data science at New York University who studies similarities and differences in how humans and machines learn, said that Tishby’s findings represent “an important step towards opening the black box of neural networks,” but he stressed that the brain represents a much bigger, blacker black box.

Our adult brains, which boast several hundred trillion connections between 86 billion neurons, in all likelihood employ a bag of tricks to enhance generalization, going beyond the basic image- and sound-recognition learning procedures that occur during infancy and that may in many ways resemble deep learning.


As a busy mother of three, Shannel Boon has always been passionate about caring for others.  In January 2017, she decided to turn her passion into her profession when she enrolled in the Health Care Aide program offered through Campus Alberta Central.

For Shannel, her decision to return to school at this stage of life was influenced by tragedy, as she unexpectedly lost her husband in August of 2016.  “When he passed away, I started to consider how I might need to make a change to support my children and to create a new career path for me.”

Once she began her courses, Shannel found tremendous benefit in learning from her two experienced instructors, located in Drumheller.  She also connected with her classmates through in-class and practical learning, and she looks forward to applying her skills in her month-long clinical experience.  “The hands-on and clinical components are such valuable parts of this program,”

How the Web works

This theory is not essential to writing web code in the short term, but before long you'll really start to benefit from understanding what's happening in the background.

In addition to the client and the server, we also need to say hello to: When you type a web address into your browser (for our analogy that's like walking to the shop): Real web addresses aren't the nice, memorable strings you type into your address bar to find your favorite websites.

Basically, when data is sent across the web, it is sent as thousands of small chunks, so that many different web users can download the same website at the same time.

What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland.

The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion —  fitting inside both.

It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Let’s walk through how computer scientists have moved from something of a bust — until 2012 — to a boom that has unleashed applications used by hundreds of millions of people every day.

Back in that summer of ’56 conference the dream of those AI pioneers was to construct complex machines — enabled by emerging computers — that possessed the same characteristics of human intelligence.

So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming.

But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

Attributes of a stop sign image are chopped up and “examined” by the neurons —  its octogonal shape, its fire-engine red color, its distinctive letters, its traffic-sign size, and its motion or lack thereof.

In our example the system might be 86% confident the image is a stop sign, 7% confident it’s a speed limit sign, and 5% it’s a kite stuck in a tree ,and so on — and the network architecture then tells the neural network whether it is right or not.

Still, a small heretical research group led by Geoffrey Hinton at the University of Toronto kept at it, finally parallelizing the algorithms for supercomputers to run and proving the concept, but it wasn’t until GPUs were deployed in the effort that the promise was realized.

It needs to see hundreds of thousands, even millions of images, until the weightings of the neuron inputs are tuned so precisely that it gets the answer right practically every time — fog or no fog, sun or rain.

Today, image recognition by machines trained via deep learning in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans.

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Gradient descent, how neural networks learn | Deep learning, chapter 2

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