Tensorflow based a deep learning sequential model that will fit circular data Resources

Oct 5, 2022 · Our goal with CircularNet is to develop a robust and data-efficient model for waste/recyclables detection, which can support the way we identify, sort, manage, and recycle materials across the waste management ecosystem.

Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources

Jan 24, 2023 · You will see how PyCircular be used to effectively handle the periodic nature of circular data and compute meaningful measures of central tendency and dispersion. You will also learn how the library’s visualization tools can help to better understand and interpret circular data.

Dec 1, 2020 · I have been messing around in tensorflow playground. One of the input data sets is a spiral. No matter what input parameters I choose, no matter how wide and deep the neural network I make, I cannot fit the spiral. How do data scientists fit data of this shape?

Using cifar10 data set for classification experiment, circle loss is better than am softmax loss. (Python 3.7.4, tensorflow 2.1)

If we're going to model our classification data (the red and blue circles), we're going to need some non-linear lines. 🔨 Practice: Before we get to the next steps, I'd encourage you to play around with the TensorFlow Playground (check out what the data has in common with our own classification data) for 10-minutes. In particular the tab ...

Let's start by importing TensorFlow as the common alias tf. For this notebook, make sure you're using version 2.x+. We could start by importing a classification dataset but let's practice...

In this Study, Convolutional Neural Network (CNN) and SoftMax classifier are used as deep learning artificial neural network. The results show that the most accurate classification rate is obtained using the ReLu activation function.

Basically, you create a random matrix K K with columns equal to number of old features, d d, and rows equal to number of new features d′ d ′ (I had to use d′ = 300d d ′ = 300 d). Also, create a random bias vector b b with length equal to d′ d ′. And you need a non-linear activation function f f.