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Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different. Plus, how AI and IoT are inextricably connected. We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like “Machine Learning” and “Deep Learning,” sometimes used interchangeably with artificial intelligence. As a result, the difference between artificial intelligence, machine learning, and deep learning can be very unclear. I’ll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they’re different. Then, I’ll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT exp…

Training your model is hands down the most time consuming and expensive part of machine learning. Training your model on a GPU can give you speed gains close to 40x, taking 2 days and turning it into a few hours. However, this normally comes at a cost to your wallet. The other day I stumbled upon a great tool called Google Colab. I would describe Colab as the google docs equivalent of Jupyter notebooks. Colab is aimed at being an education and research tool for collaborating on machine learning projects. The great part is, that it’s completely free forever. There is no setup to use it. I didn’t even need to log in. (I was already logged into my google account) The best part is that you get an unlimited supply of 12 hours of continuous access to a k80 GPU, which is pretty powerful stuff. (You get disconnected after 12 hours, but you can use it as many times as you want) I want our focus to be training on a GPU and Colab specific so the notebook is extremely bare bones. The f…

Recently , I started reading a book  Structures : Or Why Things Don't Fall Down by
J. E. Gordon. This book talks about how life has evolved right from the age of when life was unicellular and completely dependent on water for existence. I particularly like the section where structure is defined as something which can sustain load. This book was originally published in year 1978.

I wrote a small summary of the first chapter just to make sure that my thoughts are coherent around this matter. I am sharing the summary here.

As, we all know if the engineering structures breaks, then people are more likely to get killed . Obviously, we don't want that to happen. Engineers do well to investigate the reason behind the fall of the structure. But the problem is when they share the reason why did that happen they talk in a very strange language which doesn't seem to like an interesting one from the get-go.

But, well structures will remain part of our lives  forever that we cannot affo…

For the past year, we’ve compared nearly 8,800 open source Machine Learning projects to pick Top 30 (0.3% chance). This is an extremely competitive list and it carefully picks the best open source Machine Learning libraries, datasets and apps published between January and December 2017. Mybridge AI evaluates the quality by considering popularity, engagement and recency. To give you an idea about the quality, the average number of Github stars is 3,558.
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Python Projects of the Year (avg. 3,707 ⭐️): Here(0 duplicate)Learn Machine Learning from Top Articles for the Past Year: Here(0 duplicate) Open source projects can be useful for data scientists. You can learn by reading the source code and build something on top of the existing projects. Give a plenty of time to play around with Machine Learning projects you may have missed for the past year…