📌 Monte Carlo Methods for Solving Reinforcement Learning Problems

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-09-04 | ⏱️ Read time: 20 min read

Dissecting “Reinforcement Learning” by Richard S. Sutton with Custom Python Implementations, Episode III

📌 Hands-On Global Optimization Methods, with Python

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-04 | ⏱️ Read time: 15 min read

Four methods to find the maximum (or minimum) of your black box objective function

📌 How to Train a Vision Transformer (ViT) from Scratch

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-09-04 | ⏱️ Read time: 13 min read

A practical guide to implementing the Vision Transformer (ViT)

📌 Batch And Streaming Demystified For Unification

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-09-04 | ⏱️ Read time: 29 min read

Understand how batch can be considered a subset of streaming and why data engineering should…

📌 How I Used Clustering to Improve Chunking and Build Better RAGs

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-04 | ⏱️ Read time: 8 min read

It’s both fast and cost-effective

📌 Approximating Stochastic Functions with Multivariate Outputs

🗂 Category:

🕒 Date: 2024-09-04 | ⏱️ Read time: 25 min read

A generic approach for training probabilistic machine learning models

📌 Diving Deeper with Structured Outputs

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2024-09-03 | ⏱️ Read time: 10 min read

Enhancing our understanding and optimal usage of structured outputs

📌 Here Comes Mamba: The Selective State Space Model

🗂 Category: DEEP LEARNING

🕒 Date: 2024-09-03 | ⏱️ Read time: 22 min read

Part 3 – Towards Mamba State Space Models for Images, Videos and Time Series

📌 Using Generative AI To Get Insights From Disorderly Data

🗂 Category:

🕒 Date: 2024-09-03 | ⏱️ Read time: 41 min read

Best practices for using Large Language Models to extract actionable insights even with poor metadata

📌 Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training

🗂 Category:

🕒 Date: 2024-09-03 | ⏱️ Read time: 20 min read

This blog post will go line-by-line through the code in Section 3 of Andrej Karpathy’s…

📌 5 Pillars for a Hyper-Optimized AI Workflow

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-09-03 | ⏱️ Read time: 8 min read

A gentle introduction to a methodology for creating production-ready, extensible & highly optimized AI workflows

📌 Information in Noise

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-03 | ⏱️ Read time: 4 min read

Two Techniques for Visualizing Many Time-Series at Once

📌 Subway Route Data Extraction with Overpass API: A Step-by-Step Guide

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-03 | ⏱️ Read time: 11 min read

Simplify Geodata Extraction from OpenStreetMaps via the Overpass API

📌 Use R to build Clinical Flowchart with shinyCyJS

🗂 Category:

🕒 Date: 2024-09-03 | ⏱️ Read time: 6 min read

Customizable R package for Graph / Network visualization

📌 Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-02 | ⏱️ Read time: 10 min read

Six ways of matchmaking categories and numbers

📌 An Easy Way to Remove Tourists from Photos

🗂 Category: PYTHON

🕒 Date: 2024-09-02 | ⏱️ Read time: 9 min read

Image cleanup with Python, PIL, and OpenCV

📌 Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read

Solving the Restricted Quadratic Multi-Knapsack Problem (RQMKP) with mathematical programming and Python

📌 Create Your Own Meal Planner Using ChatGPT

🗂 Category: CHATGPT

🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read

A brief guide to prompt engineering

📌 Training AI Models on CPU

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-09-01 | ⏱️ Read time: 16 min read

Revisiting CPU for ML in an Era of GPU Scarcity

📌 Building Scalable Data Platforms

🗂 Category: ANALYTICS

🕒 Date: 2024-09-01 | ⏱️ Read time: 14 min read

Data Mesh trends in data platform design

📌 The DIY Path to AI Product Management: Picking a Starter Project

🗂 Category: CHATGPT

🕒 Date: 2024-08-31 | ⏱️ Read time: 8 min read

Building real-world skills through hands-on trial and error.