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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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Building Recommendation System with Deep Reinforcement Learning and Neo4j
Introduction In this tutorial We will be building a movie recommendantion enine leveraging a blend of graph-based machine learning and deep reinforcement learning (DRL). This post details an approach using…
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Bayesian Neural Networks for Predicting Novel Unseen Classes
Introduction Bayesian Neural Networks (BNNs) provide a unique approach to neural network modeling by incorporating uncertainty into predictions. This tutorial explores the application of BNNs in predicting novel unseen classes…
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LSTM Autoencoder for Anomaly Detection in Python with Keras
Using LSTM Autoencoder to Detect Anomalies and Classify Rare Events So many times, actually most of real-life data, we have unbalanced data. Data were the events in which we are…
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Clustering the Manifold of the Embeddings Learned by Autoencoders
Autoencoder with Manifold Learning for Clustering Whenever we have unlabeled data, we usually think about doing clustering. Clustering helps find the similarities and relationships within the data. Clustering algorithms like…
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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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