Naresh Nishad articles - 54 total
Day 52: Monitoring LLM Performance in Production
Introduction Deploying Large Language Models (LLMs) is only half the battle. Once in...
Day 51: Containerization of LLM Applications
Introduction As Large Language Models (LLMs) become integral to applications, deploying...
Day 50: Building a REST API for LLM Inference
Introduction Large Language Models (LLMs) like GPT and BERT have immense potential, but...
Day 49: Serving LLMs with ONNX Runtime
Introduction Serving Large Language Models (LLMs) efficiently is crucial for real-world...
Day 48: Quantization of LLMs
Introduction Quantization is a powerful technique for optimizing the deployment of Large...
Day 47: Model Compression for Deployment
Introduction Deploying Large Language Models (LLMs) in real-world applications often...
Day 46: Adversarial Attacks on LLMs
Introduction As Large Language Models (LLMs) become increasingly pervasive, understanding...
Day 45: Interpretability Techniques for LLMs
Introduction As Large Language Models (LLMs) grow increasingly powerful, understanding...
Day 44: Probing Tasks for LLMs
Introduction Probing tasks are essential tools for understanding the inner workings of...
Day 43: Evaluation Metrics for LLMs
Introduction Evaluating the performance of Large Language Models (LLMs) is a critical step...
Day 42: Continual Learning in LLMs
Introduction In the rapidly evolving field of AI, the ability to learn and adapt over time...
Day 41: Multilingual LLMs
Introduction With the rise of globalization, the ability to process and generate text in...
Day 40: Constrained Decoding with LLMs
Introduction Constrained Decoding is a powerful technique in NLP that ensures generated...
Day 39: Summarization with LLMs
Introduction Text Summarization is an essential NLP task where a model condenses long...
Day 38: Question Answering with LLMs
Introduction Question Answering (QA) is a fascinating NLP task where a model answers...
Day 37: Named Entity Recognition (NER) with LLMs
Introduction Named Entity Recognition (NER) is a crucial Natural Language Processing (NLP)...
PostgreSQL Connection Pooling vs No Pooling: Benchmark Analysis
Introduction Database connection management is critical for application performance....
Day 36: Text Classification with LLMs
Introduction Text classification is a fundamental NLP task where we assign categories or...
Day 35 - BERT: Bidirectional Encoder Representations from Transformers
Introduction Today's focus for Day 35 of the #75DaysOfLLM journey was on BERT...
Day 34 - XLNet: Generalized Autoregressive Pretraining for Language Understanding
Introduction In today's exploration of the #75DaysOfLLM journey, I delved into XLNet, a...
Day 33 - ALBERT (A Lite BERT): Efficient Language Model
Introduction Today’s exploration on Day 33 of my 75DaysOfLLM journey focuses on ALBERT (A...
Day 32 - Switch Transformers: Efficient Large-Scale Models
Introduction Switch Transformers are a significant innovation in deep learning,...
Day 31: Longformer - Efficient Attention Mechanism for Long Documents
Introduction As large language models continue to be applied to tasks involving lengthy...
Day:30 Reformer: Efficient Transformer for Large Scale Models
Introduction As the scale of language models continues to expand, so do the demands on...
Day 29: Sparse Transformers: Efficient Scaling for Large Language Models
Introduction As large language models (LLMs) continue to grow, their computational and...
Day 28: Model Compression Techniques for Large Language Models (LLMs)
Introduction As large language models (LLMs) grow in size, they demand more memory,...
Day 27: Regularization Techniques for Large Language Models (LLMs)
Introduction As LLMs (Large Language Models) grow in complexity and scale, regularization...
Day 26: Learning Rate Schedules
Introduction In deep learning, setting the right learning rate is crucial for model...
Day: 25 Optimizer Algorithms for Large Language Models (LLMs)
Introduction Training large language models (LLMs) effectively requires selecting the...
Day 24: Gradient Accumulation
Introduction In the world of deep learning, training large models with limited GPU memory...