TechNuggets VS examples

Next, we will create our components. Nothing too crazy, just a text component and a notifications component. Each one will have its own css file in the src/components directory. (you can check out the code at this repo) Eventually, our frontend will look like something like this

I've have all the code used in this post available in this repo

I've created a notebook in this Github repo to demo some of Plotly basic capabilities and I highly recommend checking out the official documentations for examples of each plot type and to discover lots of cool stuff that you can put in your notebook/site 🙂.

If you are eager to see a complete example on a Tkinter gui, I've created and example which is more or less simulating the actual situation which I told you about earlier in this post. You can find it in this github repo. I've separated my business logic (killer_script.py) from my GUI (myapp.py) and I used a launcher module (app_launcher.py) for further modularity. I did my best commenting the py files 🙂 but you will always find something that I missed and you didn't totally understand! That's completely normal. You only have to look it up online or in the docs! If that didn't work, just drop it below in the comments and I'll try my best to make it simpler for you 😉

Pandas can import data from a wide variety of data sources. Usually it uses the function read_x where "x" is csv, excel, json, ..., etc. In this tutorial, we will use a csv (comma separated values) file representing a transactions (purchases) log in a grocery store. Each row represents one item in a single transaction. One transaction can have multiple items. Sometimes, manual entries can happen 😬. You can find the sample dataset and all the code in this tutorial in this Github repo. The goal of this tutorial is to wrangle this file to find out what is the total revenue per item category and the top 3 purchased items.

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.

The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.

10. TensorFlow - Make Machine Learning Work for You 🤖

AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.

#2 TensorFlow

I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?