ml

A #ML algorithm cheat sheet - helping narrow down to a certain set of #algorithm grouping depending on the problem at hand and what we are trying to solve from a business perspective. ML algorithm cheat sheet Figure 2 shows what additional characteristics we need to consider when choosing the right ML algorithm for your situation at hand. This is something that cannot be generic and is very situational. Characteristics in selecting ML algorithms If you find this useful, I would also recommend reading “ How to select algorithms ” which is detailed as part of Azure ML designer ....

Floating point calculations are slow for computers (specifically CPUs); possibly representing the same struggle for many humans. :) I remember a time when a FPU (floating point unit) was an upgrade and one had to pay extra to get one. Very useful when you needed that extra precision in computing - and in my head, it always seemed like the Turbo button. :) For most #ML workloads and computations, precision isn’t the most important criteria; with every increasing data and parameters (looking at you GPT-3 with 45 TB of data and 175 billion parameters!...

Sometimes one needs a quick snapshot of what are the options to think through and I really like this for that. Machine Learning Algorithms

The naming is unfortunate when talking about #AI. There isn’t anything about intelligence - not as we humans know of it. If we can rewind back to the 50’s we can perhaps rename it to something like Computational Intelligence, which is more accurate. And although I have outlined the difference between some of the elements of AI in the past, I wanted to get back to what the intent was and how this area started....

Regularization is a fundamental concept in Machine Learning (#ML) and is generally used with activation functions . It is the key technique that help with overfitting. Overfitting is when an algorithm or model ‘fits’ the training data too well - it seems to good to be true. Essentially overfitting is when a model being trained, learns the noise in the data instead of ignoring it. If we allow overfitting, then the network only uses (or is more heavily influenced) by a subset of the input (the larger peaks), and doesn’t factor in all the input....

Neural Networks, today, help in a great set of tasks, that until very recently wasn’t possible at all - be it from computer vision, to medical diagnosis, to speech translation and forms a key cornerstone to a lot of ‘magic’ that Machine Learning and AI offers today. I did blog about Neural Network types (and MarI/O) sometime back ; I surely cannot take credit for creating these three cheat sheets but they are awesome and hope you get to use and enjoy them too....

Seem like my training data for the car - perhaps a hint of #bias. 😂 #GeekyJokes #ML #AIJokes

Neural networks have a very interesting aspect – they can be viewed as a simple mathematical model that defines a function. For a given function $f(x)$ which can take any input value of $x$, there will be some kind a neural network satisfying that function. This hypothesis was proven almost 20 years ago (“ Approximation by Superpositions of a Sigmoidal Function ” and “ Multilayer feedforward networks are universal approximators ”) and forms the basis of much of #AI and #ML use cases possible ....

I was looking at something else and happen to stumble across something called Netron , which is a model visualizer for #ML and #DeepLearning models. It is certainly much nicer than for anything else I have seen. The main thing that stood out for me, was that it supports ONNX , and a whole bunch of other formats (Keras, CoreML), TensorFlow (including Lite and JS), Caffe, Caffe2, and MXNet. How awesome is that?...

Someone recently asked me, what are some of the use cases / examples of machine learning. Whilst, this might seem as an obvious aspect to some of us, it isn’t the case for many businesses and enterprises – despite that they uses elements of #ML (and #AI) in their daily life – as a consumer. Whilst, the discussion gets more interesting based on the specific domain and the possibly use cases (of course understanding that some might not be sure f the use case – hence the question in the first place)....

Over the last few weeks, I built a self-driving car - which essentially is a remote control Rx car that uses a raspberry pi running Python, TensorFlow implementing a end-to-end convolution neural network (CNN) Of course other than being a bit geeky, I do think this is very cool to help understand and get into some of the basic constructs and mechanics around a number of things - web page design, hardware (maker things), and Artificial Intelligence principles....

If you were trying to pull the latest source code on your Raspberry Pi for donkeycar, and get the following error, then probably your clock is off (and I guess some nonce is failing). This can happen if your pi had been powered off for a while (as in my case), and it’s clock is off ( clock drift is a real thing) :). fatal: unable to access 'https://github.com/wroscoe/donkey/': server certificate verification failed....

Can #AI make me look (more) presentable? The jury is out I think. This is called style transfer, where the style/technique from a kind of painting (could be a photos too) is applied to an image, to create a new image. I took this using the built-in camera on my machine sitting at my desk and then applying the different kind of ‘styles’ on it. Each of these styles are is a separate #deeplearning model that has learned how to apply the relevant style to a source image....

Thinking about #machinelearning? It will be helpful to understand some numerical computations and concepts that affect the #ML algorithm. One might not interact with these directly, but we surely can feel the effect. The things you need to think about are: 1. Overflow and underflow - thinking of them as rounding up or down errors that shift the functions enough, and compounded across the iterations cam be devastating. Of course can also easily get to division by zero....

I know I have had to explain this a lot in most #AI related conversations that I have had - and lately those have been quite a lot. In my experience, most people use these terms interchangeably when they are meaning one over the other. Whilst they all are (inter)related and one might help trigger the other, they are still fundamentally different and at some point, it is good to understand the differences....