Facing economic headwind, tech giants like Amazon, Meta, and Twitter cut thousands of jobs. What does that mean for the future of AI? — Until very recently, companies were fighting to attract and retain quality staff in data science. Online business thrived during times of lockdown, with the world suddenly relying on parcel deliveries, cloud environments, online meeting spaces, and virtual pastimes. …

The potential of data analytics is often held back by ancient and diverse legacy system scattered across the company. However, data and operations don’t need to stay intertwined forever. — Data-driven decision-making — the sales pitch Stating that data radically transforms the way businesses operate is hardly a revelation. Whether it is high-frequency sensor data, real-time stock market prices or detailed user logs — we track, collect and store data at a scale unprecedented in history. The reason: these vast piles of data hide value. …

Prepare yourself for the exams DS101, DS201 and the practical exam — The DataCamp trajectory DataCamp is a great way to build a data science foundation. This article explains how to complete the certification trajectory and solidify that foundation. A number of months ago, I enrolled in the Data Scientist career track (Python) of DataCamp; an activity I pursued on the side. …

A gradient-based reinforcement learning algorithm to learn deterministic policies for continuous action spaces — This article introduces Deep Deterministic Policy Gradient (DDPG) — a Reinforcement Learning algorithm suitable for deterministic policies applied in continuous action spaces. By combining the actor-critic paradigm with deep neural networks, continuous action spaces can be tackled without resorting to stochastic policies.

A Python implementation of Q-learning to solve the Taxi-v3 environment from OpenAI Gym in an animated Jupyter Notebook — The goal of the Taxi Environment in OpenAI’s Gym — yes, from the company behind ChatGPT and Dall⋅E — is simple and straightforward, making for an excellent introduction to the field of Reinforcement Learning (RL). This article provides a step-to-step guide to implement the environment, learn a policy using tabular…

Why we let randomness dictate our action selection in Reinforcement Learning — If you are used to deterministic decision-making policies (e.g., as in Deep Q-learning), the need for and use of stochastic policies might elude you. …

Harness yourself against these shortcomings encountered in everyday RL algorithms — Reinforcement Learning algorithms such as Q-learning and REINFORCE have been around for decades and their textbook implementations are still widely used. Unfortunately, they exhibit some fundamental flaws, which greatly increase the struggle of learning a good policy. This article addresses three major shortcomings of classical Reinforcement Learning algorithms, along with…

The groundbreaking services of OpenAI have taken the world by storm, but how will the company itself profit? — With the wildly successful launches of Dall-E 2 and ChatGPT, OpenAI might be the hottest tech company of 2022. Given that their services predominantly seem free of charge, what does the company get out of it? Currently, the internet is being flooded with articles and posts on ChatGPT and, earlier…

What happens if the most successful techniques in Deep Q-Learning are combined into a single algorithm? — Reinforcement Learning has come a long way since the era of classical tabular Q-learning. Deep Q-Learning Networks (DQN) drove a revolution in the field, enabling powerful generalizations of states. By replacing a Q-table (storing the values of all state-action pair) with a powerful neural network (theoretically able to transform any…