<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Home on imflash217</title><link>https://imflash217.github.io/portfolio/</link><description>Recent content in Home on imflash217</description><generator>Hugo -- 0.146.0</generator><language>en-us</language><lastBuildDate>Fri, 23 Jun 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://imflash217.github.io/portfolio/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Powered Autonomous Agents</title><link>https://imflash217.github.io/portfolio/2023-06-23-llm-autonomous-agents/</link><pubDate>Fri, 23 Jun 2023 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/2023-06-23-llm-autonomous-agents/</guid><description>An overview of building autonomous agents with LLM as the core controller, covering planning, memory, and tool use components.</description></item><item><title>numpy v/s pandas</title><link>https://imflash217.github.io/portfolio/til/042123/</link><pubDate>Fri, 21 Apr 2023 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/til/042123/</guid><description>numpy uses row-major, pandas uses column-major access - a subtle performance difference.</description></item><item><title>The Transformer Family Version 2.0</title><link>https://imflash217.github.io/portfolio/2023-01-27-the-transformer-family-v2/</link><pubDate>Fri, 27 Jan 2023 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/2023-01-27-the-transformer-family-v2/</guid><description>A comprehensive overview of the Transformer architecture and its many variants, covering attention mechanisms, positional encoding, efficient attention patterns, and applications to reinforcement learning.</description></item><item><title>Hash Tables</title><link>https://imflash217.github.io/portfolio/algorithms/hashtables/</link><pubDate>Fri, 29 Jan 2021 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/algorithms/hashtables/</guid><description>Deep dive into hash tables - hashing, collision resolution via chaining and open addressing, and hash functions.</description></item><item><title>Electromagnetic Spectrum, properties of material medium &amp; its effects</title><link>https://imflash217.github.io/portfolio/blogs/physics/blog_01282021/</link><pubDate>Thu, 28 Jan 2021 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/physics/blog_01282021/</guid><description>How EM radiation interacts with matter across the spectrum - from radio waves to X-rays and ionizing effects.</description></item><item><title>Awesome List</title><link>https://imflash217.github.io/portfolio/awesome/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/awesome/</guid><description>Curated list of resources on positional encoding, batch norm, transformers, and deep learning papers.</description></item><item><title>CS224N - Lecture 1</title><link>https://imflash217.github.io/portfolio/nlp/cs224n/cs224n_1/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/nlp/cs224n/cs224n_1/</guid><description>Stanford CS224N Lecture 1 - word meaning, distributional semantics, and word vectors.</description></item><item><title>Design Patterns</title><link>https://imflash217.github.io/portfolio/python/design_patterns/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/python/design_patterns/</guid><description>Strategy pattern in Python using ABC and namedtuples for flexible discount policies.</description></item><item><title>DETR</title><link>https://imflash217.github.io/portfolio/transformers/detr/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/transformers/detr/</guid><description>DETR - end-to-end object detection using transformers with bipartite matching loss.</description></item><item><title>Einops - Part 1</title><link>https://imflash217.github.io/portfolio/blogs/deep_learning/einops/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/deep_learning/einops/</guid><description>Introduction to einops - readable tensor operations with rearrange, reduce, and repeat replacing cryptic transposes.</description></item><item><title>Einops - Part 2: Deep Learning Architectures</title><link>https://imflash217.github.io/portfolio/blogs/deep_learning/einops2/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/deep_learning/einops2/</guid><description>Building ConvNets and Multi-Head Attention with einops - comparing traditional PyTorch vs einops implementations.</description></item><item><title>Environment &amp; Distribution Shift</title><link>https://imflash217.github.io/portfolio/blogs/deep_learning/dl_04_09/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/deep_learning/dl_04_09/</guid><description>Notes on covariate shift, label shift, and concept shift - how to detect and correct distribution changes.</description></item><item><title>Linked List Q&amp;A</title><link>https://imflash217.github.io/portfolio/algorithms/qa/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/algorithms/qa/</guid><description>Interview-style Q&amp;amp;A covering linked list fundamentals - nodes, pointers, head/tail, and common operations.</description></item><item><title>Loss Functions</title><link>https://imflash217.github.io/portfolio/blogs/deep_learning/loss/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/deep_learning/loss/</guid><description>Cross entropy loss explained with NumPy implementation for multiclass classification tasks.</description></item><item><title>ML System Design - Overview</title><link>https://imflash217.github.io/portfolio/mlsd/01_overview/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/mlsd/01_overview/</guid><description>When to use ML and when not to - cost-effectiveness and practical adoption guidelines.</description></item><item><title>Naïve Bayes Classifiers</title><link>https://imflash217.github.io/portfolio/nlp/naive_bayes/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/nlp/naive_bayes/</guid><description>Multinomial Naive Bayes for text classification - intuition, math, and implementation.</description></item><item><title>Notes</title><link>https://imflash217.github.io/portfolio/blogs/notes/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/notes/</guid><description>Practical tips on LR schedulers, optimizers, and training tricks for deep learning models.</description></item><item><title>Probability Theory - Introduction</title><link>https://imflash217.github.io/portfolio/blogs/prob/intro/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/prob/intro/</guid><description>Bayesian probability foundations - the red/blue box experiment, random variables, and decision theory basics.</description></item><item><title>Python Snippets</title><link>https://imflash217.github.io/portfolio/gists/python_snippets/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/gists/python_snippets/</guid><description>MappingProxyType, walrus operator, &lt;strong>slots&lt;/strong> - useful Python snippets and idioms.</description></item><item><title>Regular Expressions (RegEx)</title><link>https://imflash217.github.io/portfolio/nlp/regex/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/nlp/regex/</guid><description>Regular expression fundamentals - pattern syntax for text search and processing.</description></item><item><title>Sorting Algorithms</title><link>https://imflash217.github.io/portfolio/algorithms/sorting/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/algorithms/sorting/</guid><description>Insertion sort, binary insertion sort, merge sort, and heap sort with pseudocode and complexity analysis.</description></item><item><title>Speech &amp; Language Processing</title><link>https://imflash217.github.io/portfolio/nlp/nlp_book/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/nlp/nlp_book/</guid><description>Notes on text normalization, tokenization, and NLP fundamentals from Jurafsky and Martin.</description></item><item><title>Speech Processing - Lecture 3</title><link>https://imflash217.github.io/portfolio/speech/lecture-3/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/speech/lecture-3/</guid><description>Speech processing lecture - signal-to-symbol transformation, prosody, and language knowledge.</description></item><item><title>Tensorflow Tutorial</title><link>https://imflash217.github.io/portfolio/blogs/deep_learning/blog_tf_v1/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/deep_learning/blog_tf_v1/</guid><description>Hands-on TensorFlow v1.0 tutorial - variables, sessions, training algorithms, and building a neural network.</description></item><item><title>🌩️ CDN (Content Delivery Network)</title><link>https://imflash217.github.io/portfolio/blogs/system_design/cdn/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/system_design/cdn/</guid><description>Content Delivery Networks explained in Hindi - caching, static content delivery, and CDN architecture.</description></item><item><title>🧑🏻‍💻 DL Interviews</title><link>https://imflash217.github.io/portfolio/blogs/deep_learning/dl_interviews/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://imflash217.github.io/portfolio/blogs/deep_learning/dl_interviews/</guid><description>DL interview prep - maximum entropy, data augmentation, overfitting, and probability of success problems.</description></item></channel></rss>