date_range 22/09/2024 14:15

Distributed training is crucial for scaling machine learning (ML) models, especially for tasks involving large datasets or complex architectures. The process splits the training workload across multiple machines or GPUs, enabling faster training and greater efficiency. Here’s a breakdown of the key strategies, tools, and platforms that make distributed training effective.

date_range 23/12/2023 19:15

With the advent of large pre-trained language models like BERT and GPT-3, fine-tuning has emerged as a widely adopted method for transfer learning research. This approach entails customizing a pre-trained model for a specific task through training on a more modest dataset containing task-specific labeled data.

date_range 03/12/2023 00:32

Large language models (LLMs) are language models that can recognize, summarize, translate, predict, and generate content using very large datasets. They take text as input and predict what words or phrases are likely to come next. They are built using complex neural networks and trained on massive amounts of text data.

date_range 18/11/2023 21:48

The introduction of the Transformer model initially aimed to address the limitations of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in machine translation. The goal was to enable parallel computation and effectively handle long-range dependencies in sequences. While attention mechanisms were previously popular in computer vision for tasks like image classification and detection, the Transformer adapted them to enhance the efficiency and scalability of neural networks, especially in sequence-to-sequence tasks such as machine translation.

date_range 02/10/2023 01:30

Embeddings are the backbone of modern machine learning, serving as powerful tools that transform raw data into meaningful representations in lower-dimensional spaces. They play a pivotal role in machine learning, providing a multitude of benefits ranging from dimension reduction, feature learning, and semantic representation to generalization, transfer learning, and computational efficiency. Recently I have joined a ML study group and I gave a presentation about the introduction to embeddings and their methods. Here is the summarization.