The machine learning industry moves at the speed of innovation. Daily developments and progress pushes the frontier of ML and AI just that bit further.
As a practising machine learning engineer, it can be challenging to keep up with the pace of developments that occurs within the industry. I find that complacency, even for a short period, can set you back months or years behind the frontier of ML.
This article details some of the steps I’m taking in 2021 to stay relevant to the industry in terms of expertise and general domain knowledge. …
I’ve always viewed and utilised Notion as a productivity and organisation tool. With Notion, I plan my Medium posting schedule, take notes on research papers, set tasks reminders and structure my daily routine.
But using Notion as an online portfolio never crossed my mind until I came across YouTube videos and articles that presented portfolios made with Notion.
Most of the examples of portfolios made with Notion were from designers and artists. I decided to create a Notion portfolio that’s focused on a machine learning discipline.
This article details the steps and reasoning I’ve taken to create a Machine Learning/Data…
Nowadays, what it takes to get a company up and running is cash injections from individuals willing to take high-level risks on ideas or product prototypes.
These individuals are known as Venture Capitalists(VCs), Angel or Seed Investors. They’ve made companies such as Facebook, CRISPR, Deliveroo, UBER, Airbnb, and numerous more a reality.
So, What does it take to get a slice of a VC’s or Angel Investors’ capital?
Compelling ideas and your network are huge components, and after spending February fundraising, your network is a huge factor that determines if you raise enough funds or not.
“Your Network Is Your…
Time flies, mostly if you’ve been stuck at home for a year due to nationwide lockdown restrictions.
For the past year, I’ve been navigating the world of Deep Learning and Computer Vision, which also meant going back and forth between programming languages, machine learning models and spending more time on StackOverflow than I would like to admit.
This article is a written recollection of various interesting aspects of my roles and responsibilities. I’ll also include learnings and experiences outside of my position that have significantly impacted my career and specialisation trajectory.
I’ve been in the machine learning industry professionally for a year now. I’ve noticed that I have a strong sense of importance the more closely my work and learning are aligned with the frontier technology.
Perhaps I’m overstretching the feeling of doing what you love as a job. Regardless of emotions, the truth is I have learnt a lot within a year, and I’m excited by how much more AI-based content is waiting to be explored.
The past week has been all about Mars. The Perseverance rover landed on Mars on 18 February, after a seven-month journey to the red planet. Perseverance also sent back the first image of Mars it took.
Thomas Smith’s article details AI techniques leveraged to colourise and produce a high-resolution replica of the first image. Check out this article below to view Mars in colour!
Perhaps you are not interested in space exploration. In that case, Thibaud Lamothe’s article on self-driving trains might grab your attention and provide a glimpse into AI's involvement within this form of transportation.
Melisa Bardhi’s article…
Learning algorithms can be tedious at times.
The algorithm topic domain is a branch of computer science that has a notorious perception of immense complexity. But algorithms come in different shapes, sizes, and levels of sophistication.
Suppose you aspire to get to a level where you can tackle complex algorithms and solve problems with them. In that case, you have to build a backlog of experience understanding and implementing less complex algorithms.
One of these ‘not too complex’ algorithms is Merge sort.
You are in for a treat with this week’s recommended articles for Data Scientists. As usual, there’s been a constant quantity of well written and informative articles from AI writers on the Medium platform.
There are two articles you must read in this week’s list. The first article addresses the difference between ML Engineers and Data Scientists' roles — important information for those currently job seeking. And the second article showcases a pragmatic utilisation of data science skills.
Data Science and it’s applicability to real estate.
Andrea cleverly utilises his data science superpowers to streamline his search for a new…
There’s a difference between smart and clever. Generally, Data Scientists and Machine learning practitioners are smart, meaning they have general technical intelligence that makes them formidable within their profession.
Clever, on the other hand, goes a step beyond intellectual capabilities. To guide actions and decisions, clever Data Scientists combine emotional cues, behavioural patterns, environmental knowledge and more during interactions.
The vast topics and disciplines associated with the Data Science field, and more broadly Machine learning field, allows for technical writers on Medium to cover interesting subjects within articles.
Below are five Data Science focused article that shouldn’t go unread. The topics covered by the presented articles range from cybersecurity to data science project management.
Note: Feel free to use the comment section to share any ML and DS article you've come across this week that is worth sharing.
This article centres on the viewpoint that specialisation is paramount to thrive within the Data Science field.