How to choose an Unsupervised Learning Technique?

Select the specific unsupervised learning technique that best suits your problem. For example, if you want are provided with unlabeled data and you want to create a particular category you can perform clustering using K-means, if you want to learn meaningful representations from unlabeled data, autoencoders could be a good choice. If you want to generate new data samples, you might consider using GANs. Some popular unsupervised learning techniques are.

• K-means is a simple and popular clustering algorithm that partitions data into K clusters based on similarity.
• Autoencoders are neural networks that aim to learn efficient representations of input data by reconstructing it from a compressed representation.
• GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously. The generator learns to generate realistic data samples, while the discriminator learns to distinguish between real and fake samples.
• SOMs are neural networks that learn to map high-dimensional data onto a lower-dimensional grid while preserving the topological properties of the input space.
• PCA is a dimensionality reduction technique that identifies the principal components (orthogonal directions) that capture the maximum variance in the data.

In this article, we are going to explore how can we implement unsupervised learning tasks using TensorFlow framework. Unsupervised learning, a branch of machine learning, discovers patterns or structures in data without explicit labels. TensorFlow users can explore diverse unsupervised learning techniques such as clustering, dimensionality reduction, and generative modelling.

To implement unsupervised learning tasks with TensorFlow, we can use various techniques such as autoencoders, generative adversarial networks (GANs), self-organizing maps (SOMs), or clustering algorithms like K-means. There are some steps to follow to implement tasks, as follows:

1. Choose an Unsupervised Learning Technique
2. Prepare Data
3. Build Model
4. Define Loss Function and Optimizer
5. Train Model
6. Evaluate Model
7. Prediction