Venture sprints
Explore an opportunity, challenge the assumptions, and work out what’s worth building next.
Rishal Hurbans
Founder, author
Curiosity,
put to work.
I’m Rishal. I build, write, and help people make sense of technology. Fifteen years in, I’m still learning.
From Brain food · Jun 2023
Brain food: The blank canvas problem
A blank canvas is vessel for expressive artwork, insightful knowledge, innovative solutions, and anything that our minds can imagine
Read the essay
Grokking AI Algorithms
Second edition · An illustrated guide
A practical, visual guide to the algorithms that power search, machine learning, neural networks, LLMs, and generative AI.
Explore the bookMake your next move with more clarity.
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From the notebook.
Ideas everyone should know
By learning about disparate concepts in the world, one can build an intuition and deeper understanding of the interconnectivity of everything; and hopefully use that more-informed view to make a positive difference.
Interacts App • Effective facilitation
Facilitating meetings and workshops can be daunting and somewhat stressful. Moreover, attendees are often quiet, disengaged, and feel like the session is a waste of their time <cue joke about useless meetings>.
Reinforcement learning with Q-learning
Like other machine learning algorithms, a reinforcement learning model needs to be trained before it can be used. The training phase centers on exploring the environment and receiving feedback, given specific actions performed in specific circumstances or states.
Intuition of reinforcement learning
Reinforcement learning (RL) is an area of machine learning inspired by behavioral psychology. The concept of reinforcement learning is based on cumulative rewards or penalties for the actions that are taken by an agent in a dynamic environment.
Introduction to purpose-specific neural networks
Artificial neural networks (ANNs) are versatile and can be designed to address different problems. Specific architectural styles are useful for solving certain problems. Think of the architecture as being the fundamental configuration of the network.
Intuition of data in artificial neural networks
To explore the workings of multi-node ANNs, consider an example dataset related to car collisions. Suppose that we have data from several cars at the moment that an unforeseen object enters the path of their movement.