nlp
A Deep Dive Into Low-Rank Adaptation (LoRA)
Introduction In the evolving landscape of large language models (LLMs) and the almost infinite number of use cases that they can help in, the ability to fine-tune them efficiently and…
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Fine-Tuning BERT for Sentiment Analysis
Introduction Using a pre-trained Language Model like BERT (Bidirectional Encoder Representations from Transformers), we can leverage contextual embeddings to enhance the ability to understand and analyze natural language text. This…
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Autoencoder with Manifold Learning for Clustering in Python
Clustering the Manifold of the Embeddings Learned by Autoencoders Whenever we have unlabeled data, we usually think about doing clustering. Clustering helps find the similarities and relationships within the data.…
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Sentiment Prediction using CNN and LSTM in Keras
Using Convolutional and Long Short-Term Memory Neural Networks to Classify IMDB Movie Reviews as Positive or Negative We will explore combining the CNN and LSTM along with Word Embeddings to…
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