How do I start a career as a deep learning engineer? What are some of the key tools and frameworks used in AI? How do I learn more about ethics in AI? Everyone has questions, but the most common questions in AI always return to this: how do I get involved? Cutting through the hype to share fundamental principles for building a career in AI, a group of AI professionals gathered at NVIDIA's GTC conference in the spring offered what may be the best place to start. Each panelist, in a conversation with NVIDIA's Louis Stewart, head of strategic initiatives for the developer ecosystem, came to the industry from very different places. But the speakers -- Katie Kallot, NVIDIA's former head of global developer relations and emerging areas; David Ajoku, founder of startup aware.ai;

Doing 500 days of AI project was a fascinating journey and enriched my life in many ways. One way was through awareness of the breadth of areas that artificial intelligence was being discussed within society. I could also more clearly see the varied applications of AI in multiple environments. After 500 days I looked back before Christmas in 2020 and I could say that I had at least the intention to get an understanding of the field of artificial intelligence. Here is a link to my article containing links to all the 500 articles on the topic of artificial intelligence.

Books and courses where two main resources that I used to get to where I am right now. Among all the resources in this section, Kaggle stands out as one of the most amazing platforms on the internet for practicing and learning machine learning! Kaggle hosts lots of data science and deep learning competitions every year with participants taking part from all over the world. There are also loads of invaluable datasets on Kaggle that you can use to train your own models, all for free. The other amazing part of this platform is where people can share their code and solution to the competitions, all in a simple Jupyter notebook which makes experimenting with the code really easy.

Note that there is also a repository of this article with all the resources clearly identified for you to follow in order as well. In my opinion, the best way to start learning anything is with short YouTube video introductions. This field is no exception. There are thousands of amazing videos and playlists that teach important machine learning concepts for free on this platform, and you should definitely take advantage of them. Here, I list a few of the best videos I found that will give you a great first introduction to the terms you need to know to get started in the field.

When it comes to breaking into the field of data science, you need to use every trick in the book to give yourself that one advantage that pushes you over the finish line. So why not try to emulate the habits of the best in the business? This article isn't a "get rich quick" method to becoming an efficient data scientist. Instead, it shows the habits that have helped the best data scientists get to where they are. It's often said that a data scientist's worth is determined by the impact they can have on an organization.

AI is currently booming and with the kind of advancements that are happening, there is no stopping. Every industry, from manufacturing, retail, pharmaceuticals, to healthcare and finance, uses AI and machine learning (a subset of AI) tools to automate mundane tasks, sift through several GBs of data to make an accurate business decision, and improve customer service amongst many other tasks. So while this is all true and evident, everyone wants to hop on the train that is artificial intelligence. Artificial Intelligence is a unique field. As a young graduate, you might not have any solid information about the field and no relevant experience. A few modules in colleges will not be much of help, and employers have a hard time looking for candidates with relevant experience.

You immigrate to a new country that speaks a different language, and start work with some of the brightest engineers in the world. Now, you're leading teams of people who are 10 or 20 years older than you, working on one of the fastest growing internet companies of the last decade. You have two options: sink or swim. That's the position Simon Eskildsen found himself in early in his career. He left his home in Denmark after high school, and moved to Canada alone to take a pre-college gap year working at Shopify. When he started, Shopify had 150 employees supporting tens of thousands of merchants. Now, it has 5,000 employees and over a million merchants.

You just released a machine learning project. It can be a new product at your start-up, a proof of concept for a client demo, or a personal project that enriches your portfolio. You are not looking for a production-grade site; you want to get the job done. So that a few users can test your product. This post is a follow-up and an update over this previous post, where I introduced raplyrics.eu,

This is a technical blog about a project I worked on using Generative Adversarial Networks. As this was a personal project, I played around with an anime dataset that I wouldn't normally use in a professional environment. Here's a link to the dataset along with a detailed write-up about models that use the dataset: I'll go into the technical details of what I did and some of the lessons I learned. Part of the project was a tool to make interacting with and learning about generative adversarial networks easier, but at this point it is not user friendly. I've found that incorporating experimental code into a tool like this instead of working with a jupyter notebook makes it much easier to repeat experiments with different settings.

Kaggle Learn bills itself as "Faster Data Science Education," a free repository of micro-courses covering an array of "[p]ractical data skills you can apply immediately." As I'm sure you are well aware, there are all sorts of free and low-cost data science education alternatives available via numerous online platforms. So why am I feeling it necessary to write about another data science learning resource? As I plan to embark on a fresh fall learning initiative -- once Those Lazy-Hazy-Crazy Days of Summer are out of my system -- I wanted to first find some concise review material for concepts I have previously learned and skills I have already acquired but which may have gone a bit rusty on me. To be clear, Kaggle Learn does not bill its micro-courses specifically as review material; however, I am so far finding that they fit this requirement for me rather well (though, admittedly, I'm still early in the process).