Certainly! Here are ten examples of underserved business plans that could benefit from a path of disruption through continuous engineering improvements: Affordable renewable energy solutions for developing countries Sustainable and eco-friendly packaging for food and consumer goods Autonomous vehicles for public transportation and ride-sharing services Low-cost and sustainable housing solutions for low-income families Electric and […]
Blog Posts
Midjourney Fun
There once was a baby raccoon who loved snuggling his mommy. His name was Racarslin.
Notes for – Building makemore Part 3: Activations & Gradients, BatchNorm
Building makemore Part 3 Jupyter Notebook makemore: part 3¶ Recurring Neural Networks RNNs are not as easily optimizable with first order gradient techniques we have available to us and the key to understanding why they are not optimizable easily is to understand the activations and their gradients and how they behave during training. In [2]: import […]
Tesla Full Self Driving
Currently it costs in the USA ~ $684/mo to drive 1000 miles with FSD subscription, insurance and energy costs. This works out to $0.684(0.69)/mile. How odd. Most cars sit around doing not that much, most of the time. As you decrease the number $684, you broaden the available market. Even starting with a couple buying […]
Gimp’s default screen grab
This is totally useless. It immediately takes a picture of the window. Which is Gimp. The “Select a region to grab” option would be a better default.
Notes for – Building makemore Part 2: MLP
Attempting to scale normalized probability counts grows exponentially You can use Multi Layer Perceptrons (MLPs) as a solution to maximize the log-likelihood of the training data. MLPs let you make predictions by embedding words close togehter in a space such that knowledge transfer of interchangability can occur with good confidence. With a vocabulary of 17000 […]
Notes for — The spelled-out intro to language modeling: building makemore
Derived from: https://github.com/karpathy/nn-zero-to-hero/blob/master/lectures/makemore/makemore_part1_bigrams.ipynb Makemore Purpose: to make more of examples you give it. Ie: names training makemore on names will make unique sounding names this dataset will be used to train a character level language model modelling sequence of characters and able to predict next character in a sequence makemore implements a services of language […]
Notes for – The spelled-out intro to neural networks and backpropagation: building micrograd
Summary of above: Backpropagation is just a recursive application of chain rule backward through the graph storing the computed derivative as a gradient (grad) variable within each node. Gotcha! We must be careful that when adding multiple of the same term we correctly derive wrt all terms instead of just a single term! Ie: b […]