2 posts tagged with "unsupervised learning"

Abstract​

The main purpose of this article is to use basic self-attention blocks to build a simple large language model for learning purposes. Due to limitations in model scale and embedding methods, the model built in this article will not be very effective, but this does not affect the ability to learn various basic concepts of language models similar to Transformer through the code provided in this article.

What happens on this page:

• get full code of a basic Large(?) Language Model (data preparation, model architecture, model training and predicting)
• understand the general architecture of transformer with a few illustrations
• understand how self regressive training works
• understand how to load very large text dataset into limited memory (OpenWebTextCorpus)
• train and observe the training procedure
• load the trained model into a simple ask-and-answer interactive script

Get full code​

Code available at github.com/visualDust/naive-llm-from-scratch

AutoEncoders​

AutoEncoders are a type of artificial neural network used primarily for unsupervised learning tasks, particularly in the field of dimensionality reduction and feature learning. Their general purpose is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction or noise reduction.

The main components of an autoencoder are:

1. Encoder: This part of the network compresses the input into a latent space representation. It encodes the input data as an encoded representation in a reduced dimension. The encoder layer is typically composed of a series of layers that gradually decrease in size.

2. Bottleneck: This is the layer that contains the encoded representation of the input data. It is the heart of the network, which holds the compressed knowledge of the input data. The bottleneck is where the dimensionality reduction takes place.

3. Decoder: The decoder network performs the reverse operation of the encoder. It takes the encoded data from the bottleneck and reconstructs the input data as closely as possible. This part of the network is typically symmetrical to the encoder, with layers increasing in size.

The objective of an autoencoder is to minimize the difference between the original input and its reconstruction, typically measured by a loss function like mean squared error. By learning to reconstruct the input data, the network learns valuable properties about the data and its structure. AutoEncoders are used in various applications like anomaly detection, image denoising, and as a pre-training step for deep learning models.

The objective of an autoencoder is to minimize the difference between the original input and its reconstruction, typically measured by a loss function like mean squared error. By learning to reconstruct the input data, the network learns valuable properties about the data and its structure.

Get full code before continue​

Code available at gist : single-file-linear-ave.py