Have you ever wondered how information is represented within a neural network?
All the inputs, outputs, and transformations are represented by data structures, referred to as tensors. Now you probably are wondering, what are really tensors?
The term “tensor” is more a mathematical generalization of many concepts rather than one specific thing.
If you’ve programmed before, you are most likely familiar with the terms “number”, “array/list”, “2D array”.
If you have studied math, you probably have heard the terms “scalar”, “vector”, and “matrix”.
Well, what do all these terms have in common? A number is the same thing as a…
Recall that supervised learning is when algorithms are trained off of labelled data. Each input passed into your model during training is a pair that includes the input object and its corresponding value. Supervised learning aims to create a mapping based on particular inputs to a specific output based on its findings in the labelled training data.
Let’s say we are training a neural network to classify images of dogs vs cats. If we see a picture of a dog, humans would classify the image as its name “dog” and vice versa with cats. …
An auto-encoder is a sequential neural network, consisting of two components, the encoder and the decoder.
Let’s say we were dealing with images. Our encoder would extract features from the image which would reduce some components like its height and width, but makes a latent representation for the image. This latent representation just means the neural network only captures the most relevant characteristics of the input.
The decoder is the part of the neural network which learns how to reconstruct the data from the encoded version. …
The brain is a miraculous organ, and that's not a secret to anyone. Our brains may only be 3 pounds, however, it's the powerhouse of intelligence, interpretation, initiating body movement, and is the steering wheel that controls all of our behaviours.
Although the brain is one of the most important organs within all living organisms, neuroscientists still don’t understand nor can comprehend how some components within the brain work. …
Today doctors have a limited view of what goes on in our body.
They can talk to us, take scans, run tests, and examine genetics, which all play as powerful tools for diagnosing an illness, but currently, there is only so much you can do in terms of early diagnoses.
However, that could all change because the future of brain-computer interfaces are on the tips of our fingers.
You might think I’m joking, but check this out:
This little thing on the finger above, is a Neural Dust sensor, about the size of a grain of sand.
One day, we…
I don’t have many clear memories from my early childhood.
In fact, most of my hazy memories consist of me playing log tag outside with my friends, sharing snacks at lunch, throwing crabapples at moving cars (yeah… my friends and I probably shouldn’t have done this), and trading Pokemon cards at recess.
Although, there is one memory that I clearly remember from my early childhood, and it’s one I’ll never forget.
It happened in the first grade, during storytime. I was bored, and instead of paying attention to my teacher, naturally, I let my mind wander around its own thoughts.
To some people, the idea of a brain-computer interface may seem alarming, if not scary.
There are a lot of dystopian ideas associated with these devices, such as that people will be able to control your mind, telepathy, A.I. will take over your brains, you’ll become a cyborg.
Although brain-comper interfaces don’t currently serve these purposes, maybe one day they will be able to do such futuristic things.
In this article, I’ll give you a brief insight into the purposes of brain-computer interfaces, and how they are being implemented today to change our future.
Before we dive into brain-computer interfaces…
The Canadian Cancer Society presents the following statistics related to brain tumours in 2020:
The Brain Tumour Charity also states that brain tumours are the biggest cancer killer of…
If you don’t already know, I belong to an amazing STEM program called The Knowledge Society. During the month of November, innovators from TKS Toronto had the opportunity in teams to come up with a solution to improve customer service at Instacart. My group decided to target the produce issue at Instacart since it has been known that some consumers have received rotten produce from shoppers.
It’s insane how you can detect pneumonia in images using deep learning models, saving an individual’s life. According to the World Health Organization, pneumonia kills around 2 million children under the age of 5 years and is repeatedly estimated as the single leading cause of child mortality. This means that pneumonia kills more children than HIV, malaria, and measles combined.
Additionally, the World Health Organization states that 95 % of childhood pneumonia occurs in developing countries, more specifically in the regions of Southeast Asia and Africa. What causes pneumonia? Bacterial and viral pathogens are the leading causes.
Bacterial and viral…
Innovator and AI enthusiast