Nova Institute

We’re hiring cleantech co-ops!

Maja Maher — Wed, 03 Jun 2020 20:42:22 +0000

We are hiring co-op students for a new cleantech research project. Candidates must be enrolled in a University or College, in electrical engineering, mechanical engineering, material science, or related fields.

We are especially encouraging women, indigenous people, people with disabilities, and recent immigrants to apply.

The placements are paid, full-time positions for 4 months, with amazing learning opportunities, and you will be working on cutting edge cleantech.

Check out the placements for electrical engineering, and mechanical engineering/material science and contact us if you are interested!

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We are now licensed to manufacture medical devices!

Maja Maher — Wed, 15 Apr 2020 17:55:11 +0000

We are happy to announce that we have received our license to import and manufacture Class 1 medical devices. This is a great step forward for us in our efforts to build our own personal protection equipment factory so we can supply 100.000 masks / month for free to Canadian health care workers.

Thanks to Health Canada who pushed through our application in record time!

#BuildOurOwn #Covid19

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COVID-19: All classes moved online

Maja Maher — Tue, 10 Mar 2020 19:26:22 +0000

Out of concern for the health of our students and staff we are suspending all in person classes as of March 10, 2020 until further notice. If you are currently enrolled in a course your instructor will contact you to arrange the best way to make up your lost class time. Our options include online lessons or making up the lost classes once the crisis is over.

Please stay safe, and stay home if you can!

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Now also in Toronto and Scarborough!

Maja Maher — Fri, 09 Aug 2019 20:47:20 +0000

Since our opening, we have been happily surprised that students would travel all the way from downtown Toronto and even Scarborough to attend our courses. To better serve our students in these areas we have decided to offer all our courses in three locations: Mississauga, Toronto, and Scarborough.

Would you like us to offer our courses in a location closer to you? Let us know!

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Machine Learning Specialization coming soon!

Maja Maher — Thu, 18 Oct 2018 17:03:22 +0000

We are pleased to announce that a 3 month Machine Learning Specialization program will be offered at Nova Institute starting Q1 2019 in our Mississauga campus. The program will be aimed at developers seeking to start a career in Machine Learning. The courses will be part time, at hours accommodating a regular work schedule. If you are interested in the program, or have any questions, please contact us!

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What is Machine Learning?

Maja Maher — Sun, 14 Oct 2018 15:55:40 +0000

Machine Learning is spreading into every sector of business. It is the new industrial revolution. Any economy that wants to remain competitive in the near future will need a lot of software developers with Machine Learning expertise. It has led to breakthroughs in medicine, art history, cars, finance, and more. But what is it? And how does it work?

Simply put Machine Learning is the ability for machines to learn a behaviour it was not specifically programmed for. And it is often used to make predictions based on a large amount of data.

Tom M. Mitchell, the author of the book Machine Learning wrote a widely quoted formal definition:

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” – Tom M. Mitchell

So to make machines learn we need to give it some experience (E), which often is a very large set of data, for example 60 000 images of handwritten digits. Then we need to define one or more tasks (T) that the machine should learn, for example recognize handwritten digits. And finally we need to measure the performance (P) of the machine. In our example we need to measure how accurately it can recognize the handwritten digits. For this we use a cost function.

The key to successfully apply machine learning to a problem is to correctly analyze the task and use the right type of machine learning algorithm for your problem, as well as defining a meaningful and quantifiable cost function.

Most machine learning tasks are either classification tasks or regression tasks. Classification categorizes an input into a predefined discrete category, while regression predicts a continuous output. Recognizing handwritten digits is a classification task. Predicting housing prices based on historical data is a regression task.

Machine learning algorithms can be divided into different types of learning:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

In supervised learning the algorithm is given a training set of data with labels. In our digit example it will be given 60 000 images of digits together with labels that says which digit category (0-9) each of the images belongs to.

In unsupervised learning the algorithm is only given the data, without any predefined labels or categories. Google news is an example of unsupervised learning where it groups together news articles that it thinks are related to the same story.

In reinforcement learning the algorithm receives reward/punishment for actions that it is performing in a dynamic environment, such as driving a car or playing a game against an opponent.

A key difference between traditional programming and machine learning is that in machine learning the answer is never definite. The machine learning algorithm will tell you “this is probably the right answer” with some measure of accuracy. This opens up possibilities that are not obtainable with a strictly True/False perspective of the world.

In the movie I, Robot Detective Del Spooner (Will Smith) asks rhetorically if a robot can write a symphony or create art. If this question intrigues you you might want to check out the DeepBach project which uses machine learning to write music in the style of Bach. Or have a go at art yourself with this intermediate level tutorial on creating art with Deep learning through neural style transfer. While those examples are not quite a symphony or art from a blank canvas they are certainly fascinating examples of the direction we are headed.

Have you seen another fascinating application of machine learning? Please share with us in the comments!

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Developer Survival Guide to Python – in 5 steps

Maja Maher — Mon, 08 Oct 2018 15:52:43 +0000

If you are a programmer and you are looking for work right now you probably know that Python is one of the most sought after job skills for developers today. And with the rise of Machine Learning and AI, demand for Python programmers is just increasing.

Chances are that you go about learning a new language by diving straight into some project and trying to figure it out as you go. That’s a great way to learn, We’re big fans of practical application. It can also be a frustrating way to learn because you keep hitting walls when things don’t go your way. That’s why we give you 5 survival tips to make your transition to Python less frustrating.

1. No Semicolons – No Curly Brackets

Python is known for its simple syntax, in fact a central “pythonic” philosophy is to not use any unnecessary characters. If you are coming from another programming language such as Java or any C dialect you will have imprinted in your spine that every line must end with a semicolon. In Python you can still do that, it will work. But it is not very pythonic.

There are also no curly brackets. There is no need for them because you must always indent your code, which brings us to the next point.

2. Forced Indents

There are two types of programmers in this world. Those who format their code well, and those who don’t. Python enforces us all into good habits, your code simply will not work if you do not indent properly. How to conform? Simply follow that little voice in your head that says “you should have indented this part”.

3. Dynamic Types – Nothing To Declare

Python is dynamically typed, which means you generally do not need to worry about what type your variables are. In fact you can even change them to another type during runtime. Your variables are declared when you assign them and Python figures out the type all by itself. So you can do things like this:

>>> a = "Hello I'm a String"
>>> b = 2

>>> type(a)

>>> type(b)


>>> a = 1 + b
>>> type(a)


>>> a
3

In the above example a starts out as a str, and is then dynamically changed to an int when it gets assigned the value ‘1 + b‘ (which equals 3).

4. Function definition

Functions are defined like this:

def my_function(self, something):
   print(something)

Remember how we said Python does not like unnecessary characters and enforces indents? This makes function definitions really simple. Just a ‘def’ and a colon and any arguments in parentheses. No curly brackets, no “private static void blah blah”. Just define your function and un-indent when your function is finished.

The strangest thing about Python function definitions is that the first argument is always “self“. You can call it what you want, but it will always be a reference to the current instance of the class. If the first argument is always self, why do we have to type it? Guido van Rossum (The maker of Python) explains that here.

When you call a function you start with argument number two in the functions argument list, because you would never pass self to the function. So calling my_function from above would look like this:

my_function("hello")

5. Class definitions

Class definitions are even more simple. Again, no curly brackets, no lengthy incantations, just:

class MyClass:

Notice that by convention classnames are written in CamelCase while functions and variables are all lowercase. And don’t forget to indent.

We hope this gives your Python journey a comfortable start, if so please like and share on facebook/twitter/linkedin/anywhere! If there are things you think we should add, please tell us in the comments.

Oh, and before you go, open your Python interpreter and type:

>>>import antigravity

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Python 2 vs Python 3

Maja Maher — Sun, 09 Sep 2018 23:27:37 +0000

One of the first things you will notice when you get started with Python its that there are two versions out there, Python 2 and Python 3. As you may have guessed Python 3 is the newest version, and it fixes fundamental design flaws in Python 2. It is already 10 years old so why does not everybody use it yet? The main reason is that Python 3 is not backwards compatible. Anyone working on projects with a codebase in Python 2 would probably want to stay with Python 2 rather than convert their code to Python 3.

So which one is right for you?

If you are just getting started with Python I suggest you go with Python 3. If you are working on legacy code you may want to stay with Python 2.x for now (support ends in 2020), but for any other reason you should go with Python 3.

Python 3 adoption rate 2017

53 % of developers reported to be using Python 3 and 47 % were still using Python 2 according to Developer Ecosystem Survey 2017

Some fundamental changes in Python 3

print is now a built-in function, not a statement..
In Python 3 the input is always returned as a string (like raw_input in Python 2), not evaluated as an expression.
Support for optional function annotations is added.
An immutable bytes type and a mutable bytearray type is introduced.
Backward-compatibility features were removed, including old-style classes, string exceptions, and implicit relative imports.
Integer division is changed: 7/2 == 3.5, while 7//2 == 3 (in Python 2 both expressions would return 3).

For a complete list of changes dive into the Python documentation.

Which version of Python are you using, and why? Let us know in the comments!

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Machine Learning with TensorFlow workshop coming soon!

Maja Maher — Mon, 25 Jun 2018 04:31:05 +0000

Machine learning is becoming an important part of the economy, not just in the software development sector but everywhere. Machines are now capable of making human like interactions, such as book appointments over the phone or act as receptionists. They can drive cars, and decide which stocks are most profitable to invest in. But machine learning is also opening up new fields in medical research, insurance companies, and even hotel services. With all this innovation emerges a need for software engineers with machine learning expertise.

This workshop will give an introduction to machine learning in general, as well as a hands-on tutorial in some technologies that make it easy to implement deep learning in real software applications, such as TensorFlow with Keras, and Pandas frameworks.

The 2-day workshop will leave you with a good basis to dig deeper into these technologies on your own. If you prefer a more thorough approach we will also offer a 6 week course including a practical portfolio project.

Both the workshop and the 6 week course will be offered during the Fall 2018 in several locations in GTA area.

If you want to be notified when a course is opening registration nearby you, please contact us.

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