Tag: Deep Learning

Understanding Vector Databases

A vector database is a type of database specifically designed to store and manage vector data using arbitrary but related coordinates to related data. Unlike traditional databases that handle scalar data (like numbers, strings, or dates), vector databases are optimized for high-dimensional data points. But first, we have to talk about vector embeddings.

Vector embeddings are a method used in natural language processing (NLP) to represent words as vectors in a lower-dimensional space. This technique simplifies complex data for processing by models like Word2Vec, GloVe, or BERT. These real-world embeddings are highly complex, often with hundreds of dimensions, capturing nuanced attributes of words.

In the vast realm of artificial intelligence, deep learning has emerged as a game-changer, especially in the field of image recognition. The ability of machines to recognize and categorize images, much like the human brain, has opened up a plethora of opportunities and challenges. Let's delve into the techniques deep learning offers for image recognition and the hurdles that come with them.

Convolutional Neural Networks (CNNs)

Technique: CNNs are the backbone of most modern image recognition systems. They consist of multiple layers of small neuron collections that process portions of the input image, called receptive fields. The results from these collections are then tiled so that they overlap, to obtain a better representation of the original image; this is a distinctive feature of CNNs.

Scores and metrics in machine learning are used to evaluate the performance of a model on a given dataset. These provide a way to understand how a model is performing and also compare different models and choose the one that performs the best.

In this article, we will focus on the IoU score, which stands for Intersection over Union. IoU is a widely used metric in the field of object detection, where the goal is to locate and classify objects in images or videos. We'll also identify limitations and solutions.

Why Use Mixed Precision Deep Learning?

As advancements have been made in the field of deep learning, the discussion about mixed-precision training of deep learning models has seen a similar increase. These improvements and natural evolution of the scope, sequence, and raw power of neural networks mean that the size of these models has had to increase to compensate accordingly. Larger and more complicated deep learning models require advancements in technology and approach.

This has led to multi-GPU setups with distributed training which can get out of hand quickly as more GPUs are integrated into training. Getting back to the basic training principles of deep learning and brushing up on fundamental techniques can ease the stress of the training phase of neural networks and optimize GPU usage. Mixed precision training or automatic mixed precision training can be a simple way to do exactly this.