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.

date_range 18/09/2023 01:12

“Machine Learning Design Patterns” authored by Valliappa Lakshmanan, Sara Robinson, and Michael Munn, from Google, is a highly recommended read for individuals engaged in machine learning and data science. This book serves as an invaluable resource, offering a comprehensive overview of best practices and design patterns for machine learning development. Within its pages, we can discover practical solutions to the common hurdles encountered in ML projects, encompassing critical areas such as data preparation, model training, and deployment. The book’s extensive coverage spans essential topics, including data preparation, model selection and training, evaluation and validation, as well as deployment and monitoring.