Generating images using text: The dawn of the AI dreamers

People in a hallway

Over the past couple of years, there has been a lot of research and developments into creating AI models that can generate images from a given text prompt. This could be thought of as a personal artist, who tries to create an artwork by following the very words of your instruction. Now, who wouldn’t want to have a personal Picasso, but as that’s impossible, we can settle with the next very possible thing — an AI Picasso 😃.

Read More

Design patterns in machine learning

According to its definition, a design pattern is a reusable solution to a commonly occurring problem. In software engineering, the concept dates back to 1987 when Beck and Cunningham started to apply it to programming. By the 2000s, design patterns — especially the SOLID design principles for OOP — were considered common knowledge to programmers.

Read More

The secret formula for MLOps success

Lab equipment

This search for the pieces of the formula is what I had to do when I started working a few months ago on the product side of TeachableHub’s machine learning deployment platform. As a novice in the field, it threw me in the deep end and it was overwhelming, to say the least. Thankfully by my side, I had my team and the people forming the awesome MLOps.community and DTC communities.

Read More

For successful AI Projects, celebrate your graveyard

Graveyard

AI teams invest a lot of rigor in defining new project guidelines. But the same is not true for killing existing projects. In the absence of clear guidelines, teams let infeasible projects drag on for months. AI projects are different from traditional software projects. They have a lot more unknowns: availability of right datasets, model training to meet required accuracy threshold, fairness and robustness of recommendations in production, and many more.

Read More

A handy list of Data Science related resources for Learning, Interviewing, and Professional…

I’ve compiled a list of useful websites and information to help you in whatever stage of the process you are, whether you’re starting your journey in data science, interviewing with companies, negotiating an offer, or just looking to continue to learn more. This includes interview prep resources, post interview compensation research, and more. I’ll also continue to update the list as I find more resources along my journey.

Read More

OpenAI warns AI behind GitHub’s Copilot may be susceptible to bias

Github Copilot

A new paper published by OpenAI reveals that Copilot might have significant limitations, including biases and sample inefficiencies. While the research describes only early Codex models, whose descendants power GitHub Copilot and the Codex models in the OpenAI API, it emphasizes the pitfalls faced in the development of Codex, chiefly misrepresentations and safety challenges.

Read More

An overview of Performance Evaluation Metrics of Machine Learning(Classification) Algorithms

Performance evaluation is the most important part of machine learning in my opinion. Because machine learning itself has become pretty easy because of all the libraries and packages. Anyone can develop machine learning without knowing much about what is going on behind the scene. Then performance evaluation can be a challenge. How do you evaluate the performance of that machine learning model?

Read More

Why you should learn Regression Analysis before Deep Learning

First, I’m not saying that linear regression is better than deep learning. Second, if you know that you’re specifically interested in deep learning-related applications like computer vision, image recognition, or speech recognition, this article is probably less relevant to you. But for everyone else, I want to give my thoughts on why I think that you’re better off learning regression analysis over deep learning. Why? Because time is a limited resource and how you allocate your time will determine how far you in your learning journey.

Read More

The Million-Dollar Question: When to stop training Deep Learning Models

One of the first decisions to be made when training deep neural networks is to select the epoch in which to stop. And it is not an easy one. If the training is stopped before the optimal time, the model will not have had time to learn the most important features from the training set and therefore provide poorly fitted solutions for both the training and the test sets.

Read More

Understanding the ubiquitous nature of Uncertainty through the Heisenberg Uncertainty Principle

The Heisenberg Uncertainty Principle is an idea from quantum physics that states you can never simultaneously know the exact position and exact momentum of an object. It proclaims that the more certain we become of an object’s position, the less certain we become of its momentum and vice versa.

Read More

Researchers open-source benchmarks measuring quality of AI-generated code

Code conversion

In recent years, large-scale AI language models have shown promise in generalizing to tasks including writing code, implying that humans’ work may be one day supplemented by AI systems. But while some studies show that language models can translate code and fix compilation issues, there’s been little work on rigorously testing the coding ability of models given general coding problems.

Read More

12 active AI Game competitions (ongoing & upcoming)

Rubic's on screen

AI game competitions are also known as AI programming competitions or bot programming competitions. They can be a great place to practice programming, algorithms, and AI/ML. The competitions vary widely in their difficulty, prizes, languages available, and feasible strategies. To help you find the right one, I’ve compiled a list of ongoing and upcoming AI game competitions to check out below (up-to-date as of May 2021).

Read More

First steps before applying reinforcement learning for trading

There are many methodologies in algorithmic trading — from automated trade entry and close points based on technical and fundamental indicators to intelligent forecasts and decision making using complex maths and, of course, artificial intelligence. Reinforcement learning here stands out as a Holy Graal — no need to do intermediate forecasts or rule creation — you just have to define a target and the algorithm will learn the exact rules by itself!

Read More

AI-powered autocomplete for Code? Microsoft and GPT-3 have you covered

AI powered autocomplete for code

Last year, Microsoft expanded its partnership with OpenAI, an artificial intelligence research firm based in San Francisco. Here’s the kicker: Microsoft now has access to an exclusive license to the GPT-3 language model. If you’ve seen AI-written articles or the text-based adventure game that flexes its AI chops, you know that this merits a second look.

Read More
1 2 3 7