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In this series, we’ll explore everything about pre-training in the Generative AI (GenAI) pipeline — including how models are trained based on specific objectives or goals, and how to design an effective pre-training pipeline. But before diving into ...
Problems with Generative/FineTuning Approach: Inefficient Data Updates: Adding new data typically requires extensive retraining or fine-tuning, which can be both time-consuming and computationally expensive. Outdated Information: Generative mod...
An Encoder-Decoder is a neural network architecture primarily used in sequence-to-sequence (seq2seq) tasks where input and output sequences can have different lengths. The model consists of two parts: Encoder: Processes the input sequence and compre...
In this article, we will explore only the basic models required to understand GenAI Comparison Table: ModelWhy It's UsedFeaturesLimitations 1. RNN (Recurrent Neural Network)To handle sequential data by maintaining a hidden state that captur...
This article covers the rest of the tokenization techniques, if you didn't get a chance to see part 1, it is here Types of Tokenization Techniques: Unigram Language Model: It is a probabilistic model that uses individual tokens (subwords or words)...
Introduction Machine Learning (ML) system design is a structured process focused on developing and implementing ML models to solve specific problems or achieve particular objectives. It includes all stages of the model lifecycle, from understanding...
Tokenization is a crucial step in Natural Language Processing (NLP) that involves breaking down text into smaller, manageable units called tokens. These tokens can be words, subwords, characters, or other meaningful units, depending on the tokenizat...
Natural Language Processing (NLP) is a sub-field of AI that enables computers to understand, process, and interact with human language. Natural Language Processing (NLP) integrates computational linguistics, machine learning, and deep learning models...