The last couple weeks I’ve started experimenting with ML. As an electronic engineer I’m focus on the embedded domain and the last years on the embedded Linux domain. The last few months the semiconductor industry has turned to ML (they like to call it AI) and from now on almost all the new CPUs and MCUs are coming out with some kind of AI accelerator. The software support for that HW is still quite bad though, so there is plenty of HW and no SW, but it will get better in the future, I guess.

That said, I though that it was the right time to get involved and I wanted to experiment with ML in the low embedded and the Linux embedded domain, providing some real-working examples and source code for everything. The result, was a series of 5 blog posts which I’ll list here with a brief description for each one.

1. [ML on embedded part 1]: In this post there’s an implementation of a naive implementation of 3-input, 1-output neuron that is benchmarked in various MCUs (stm32f103, stm32f746, arduino nano, arduino leonardo, arduino due, teensy 3.2, teensy 3.5 and the esp8266.
2. [ML on embedded part 2]: In this post I’ve implemented another naive NN with 3-input, 32-hidden, 1-output. Again the same MCUs where tested.
3. [ML on embedded part 3]: Here I’ve ported tensorflow lite for microcontrollers to build with cmake for the stm32f746 and I’ve also ported a MNIST keras model I’ve found from a book to tflite. I’ve also created a jupyter notebook that you can hand-draw a digit and then from within the notebook run the inference on the stm32.
4. [ML on embedded part 4]: After the results I got from part 3, I thought it would be interesting to benchmark ST’s x-cube-ai framework to do 1-to-1 comparisons with tflite-micro on the same model and MCU.
5. [ML on embedded part 5]: As all the previous posts were about edge ML, I’ve implemented a cloud acceleration server using a Jetson nano and I developed a simple TCP server with python that also runs inferences in the same tflite model that I’ve used also in part 3 & 4. Then I’ve written a simple firmware for the ESP8266 to send random input arrays serialized with flatbuffers to the “AI cloud server” via TCP and then get the result. I’ve run some benchmarks and did some comparisons with the edge implementation.

Although these are more interesting for embedded engineers, I think it also fits in here.

Hello All.

I am looking for some intro in E2E of Machine Learning pipelines. I will be glad if you can share some stories or links of existing case studies that describe ways of E2E implementation of machine learning processes (something like kubeflow or tensorflow extended).

I am trying to find out the golden middle or best-used practices from SRE/DevOps point of view. Thank you.

I originally posted this on /r/learnmachinelearning since it’s technically a question, but I think it goes beyond the scope of questions like “how do I set up tensorflow” and is worth putting here well.

I love tabletop RPGs, and I’m specifically curious if there are any areas of current ML technology that might allow the nerd fantasy of an independent AI dungeon master to come true, whether fully or even partially.

For those unfamiliar with tabletop RPGs, I’ll run over the basic gameplay loop, using Dungeons & Dragons as my example.

At the center of it all you have a system of core rules (easily programmable rules, in fact) which tracks character stats, specifics of movement, items, abilities, success/failure checks, and other details down to a very granular level. This part is easy for computers, evidenced by the fact that RPG video games derived from these rulesets.

However, with a tabletop RPG, the game is ran by a person known as the Dungeon Master or Game Master, who acts as a kind of referee for the rules. The Dungeon Master can take whatever request a player makes, then translate that request into something compatible with the rules.

This process of “compiling” a request into a rule check can be done for something simple, such as “I attack the orc” (damage based on a dice roll + weapon type + skill modifiers + proficiency bonus, etc.), or for something crazy and convoluted, such as “I fashion the rope in my inventory into a lasso and toss it so that it loops around the damaged stone pillar at the other side of the chasm” (dice roll based on the player’s dexterity skill), followed by “I pull the rope as hard as I can” (another roll based on their strength skill, success resulting in the pillar toppling and creating a bridge of rubble, and the player losing the rope item) followed by “I walk across the rubble to cross” (dice roll based on the player’s acrobatics ability to update the player’s position and allow them to continue the loop by interacting with things on the other side of the chasm).

So there is consistency mixed with inconsistency. A player’s input can be wild and unpredictable, but it always resolves into rule checks that are consistent, mechanical, and easily understandable by a machine. The Dungeon Master is the missing link. The adjudication of a human brain allows players to interact with the rules in a way that’s deeper and freer than clicking pre-programmed buttons to perform actions, which is why video games have never been able to replicate the feeling of a real tabletop RPG.

But now we have machine learning. What kind of AI, if any, could be used to taking ambiguous statements and convert them into standard game interactions? And to what extent could it be pushed? If it’s impossible to account for everything (which I’m guessing it is, at least for now), then what kind of sacrifices could be made on the player’s side (e.g. typing only short, simple requests in a specific format, or entering nouns and verbs into text boxes) to make it possible?

Hello,

I’m working on a project from http://www2.informatik.uni-freiburg.de/~cziegler/BX/ and I’m given a question “If I like Lord of the Rings, what else I should read?”. How would you proceed? My first thought was find other users/readers who like the book too and find what else they rated high. So, I’ve tried to create a pivot table, which ran out of memory (there’s over 1.3M rows), so I’ve create a group dataframe instead but I’m kinda in how to make a correlation. My second thought was to create clusters, find in which cluster is LOTR and recommend other books from the cluster. But I’m not sure about the later one.

Any insight would be most appreciated. Thanks!

For those struggling to understand AD and dual numbers, this article might help :)!

If I understand correctly, AD essentially computes an derivative of each operator using — for example, dual numbers — analytically exact methods, and combine them using the chain rule. This is feasible because programmable functions are defined using “primitive” operators, and we can compute their derivatives algorithmically (or analytically).

edit:

noticed the author has another multivariate example.

edit 2:

This was also helpful.

Hello everyone.

Recently, I’ve been thinking about starting a blog on Deep Learning, but when I look around, there’s just so many that are doing such a good work! There are some awesome blogs on Medium that go through ML topics both for knowledgeable and expert readers, some intriguing blog on a number of platforms that publish articles on the latest big things, so to speask, in ML, and vlogs on YouTube that are clearly spending a lot of time and resources on their videos! So, I wanted to ask you guys this. What do you think is missing in blogs? If you wanted to make a blog on ML what would you write about? I really want to somehow contribute back to this awesome community that has made it possible to learn just so much for free!

Thank you 🙂

Hello… may I question something?

I am a student studying at a foreign university lab.

I am studying stock price forecasting.

Adjusting the LSTM unit and layer is complete.

The topics(plan) of the next research were auto-keras or NLP.

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But the professor said that the topic was too broad.

he wants Step by step to build up my capabilities.

So I looked for all the paper related to stock predict.

Recent research was based on the attention mechanism or the process of using natural language like news.

The following research themes should be decided, but the gap between basic research and the latest research is too large.

I do not know what to make the next research topic.

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In the case of CNN, there are various networks such as LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet, and ResNet

However, LSTM is SOTA(LSTM: A Search Space Odyssey https://arxiv.org/abs/1503.04069 ) in RNN

So, in the case of RNN is LSTM only

I think that methodology of improving accuracy of LSTM should be the subject of research.

do you have any course for forecasting time series data?

I do not know how to get directions

Your valuable opinions and thoughts will be very much appreciated.