Mudit Bachhawat

Tag: deep learning

  • LLM-as-a-Judge for AI Systems

    Reading Time: 10 minutes

    Introduction Common Patterns of LLM-as-a-Judge Method Basic Evaluating Judge Model Improving Judge Performance Scaling Judgments Closing References

  • Keeping Up with RAGs: Recent Developments and Optimization Techniques

    Reading Time: 10 minutes

    [medium discussion] RAG Basics Indexing Indexing Inference Inference Query Query Vector DB Vector DB Response Response nn scan nn scan Embedding Embedding Prompt +Passages Prompt +… LLM LLM Retrieval Retrieval Generation Generation Documents Documents Chunking Chunking Chunks Chunks LLM LLM Embeddings Embeddings write writeText is not SVG – cannot display Chunking Embedding Model Fine-tuning Embedding…

  • Working with Speech Data

    Reading Time: 3 minutes

    This post is for people who have good understanding of deep learning, and basic understanding of data representation for images and text. In this blog, we will explore how TTS (text to speech) systems work. References:

  • Speed Up your Keras Sequence Pipeline

    Reading Time: < 1 minute

    When using tf.keras.utils.Sequence to generate batches, the data copy overhead between processed can be very high. This leads to worker processes being blocked most of the time, and decline in batch generation. A common solution is the use of shared memory to share data between processes. PyTorch uses it. With Python 3.8, you can use shared_memory…

  • Building Visual Search Engine

    Reading Time: < 1 minute

    Ever wonder how Google or Bing finds images similar to your image. The algorithms for generating text based 10 blue-links are very different from finding visually similar or related images. In this article, we will explain one such method to build a visual search engine. We will use the Caltech 101 dataset which contains images of common…

  • Kickstarting NLP, Part 1, Language Models

    Reading Time: 5 minutes

    The purpose of this series is to summarize the latest breakthroughs, problems, and solutions in the field of natural language processing and language understanding. Language Model In layman term, language model is probability distribution over words or word sequences, in a particular given context. The abstract understanding of natural language can be useful in multiple…

  • LinkedIn Data Science Interview

    Reading Time: < 1 minute

    I recently interviewed for a research engineer (vision) role at LinkedIn. In this role the candidate is expected to work on state-of-the-art computer vision algorithms to understand users and content on the platform. In this post, I’ll summarize the questions and the whole interview process. You can find the complete interview here.