Andrew Ng’s “Machine Learning Yearning” is not just another textbook; it’s a practical playbook for anyone navigating the dynamic world of AI and deep learning. This book, a “DeepLearning.AI project”, focuses on the crucial technical strategy that can save months or even years of development time. It’s about making smart decisions to get your machine learning project from an idea to a successful application. Let’s dive into 10 key insights from this essential guide, packed with examples to help you become an AI superhero! 💪

Investing with the goal of doubling your money in just one year is an aggressive strategy – one that demands identifying companies with exceptional growth potential or major catalysts on the immediate horizon. As an investor aiming high, I know such high-reward plays come with equally high risk (a stock that can double can just as easily be cut in half). With that in mind, I’ve carefully selected six U.S. stocks (in no particular order) that I’m betting on for ~100% upside over the next 12 months. Each company operates in a high-growth domain and has specific macroeconomic tailwinds and company-specific strengths that could propel its stock upward.

In data science, A/B testing is a controlled experiment method for comparing two (or more) variants of a product to see which performs better. Typically, a control (A) and one or more variations (B, C, …) are served at random to users, and their key metrics (e.g. click-through or conversion rates) are recorded. By using statistical tests on these results, we can decide which version delivers a meaningful improvement. In this post, we’ll dive deep into A/B test design and analysis. We’ll cover hypothesis formulation, randomization, sample size and power, statistical significance, and common pitfalls – with references from recent industry and academic sources. The goal is a rigorous, reproducible approach to experimentation that experienced data scientists can adopt.

History rarely repeats itself in exactly the same way, but it often rhymes. Throughout the past five centuries, powerful nations and empires have risen to global dominance, enjoyed periods of unrivaled prosperity, and eventually declined—paving the way for new powers to take their place. The cycles of rise and fall may unfold in different settings and with new technologies, but the underlying patterns remain remarkably similar.

Databricks has established itself as a powerful platform for data engineering, analytics, and machine learning. Built on Apache Spark, it provides a unified workspace for data teams to collaborate efficiently. But is it really the best choice for every use case? And do you really need 5+ years of experience to land a job where Databricks is a requirement? Let’s dive deep into what makes Databricks stand out, its downsides, and whether extensive experience is truly necessary.

Welcome to the wild world of AI in 2025, where every week feels like a decade’s worth of innovation crammed into a few days! This past week has been absolutely bonkers, with major AI breakthroughs, fierce competition, and controversial debates taking center stage.

Ah, the UK. Land of tea ☕, rain 🌧️, and now… government-mandated backdoors into your private data? Yep, you read that right. The British government has issued a Technical Capability Notice (which is just fancy spy talk for “Do what we say or else!”) demanding Apple to create a backdoor that would let them access users’ encrypted iCloud data worldwide. 🌍🔑

Imagine you have a system where you can store numbers at specific indices and quickly retrieve the smallest index containing a particular number. Sounds easy, right? 🤔 But the challenge lies in making it efficient and scalable since the index and number range can go up to 10⁹, and we can have 100,000 operations!

Assigning heights to a matrix may sound straightforward, but this problem adds a fun twist with rules that require thought and logic! 🤔 Let’s explore this engaging problem with examples, solutions, and walkthroughs! 💡

Have you ever wondered what happens when two optimal robots play a grid-based game to outsmart each other? The Grid Game is a perfect example of strategic thinking and optimization! 🧠 Let’s dive deep into this exciting problem and learn how to solve it optimally. 🚀

Welcome to another thrilling coding adventure! 🚀 Today, we’re tackling LeetCode Problem #407: Trapping Rain Water II, a mind-bending challenge that’ll have you imagining rainwater collecting in valleys of a 2D elevation map. 🌄

Imagine you’re navigating a grid where each cell has a sign directing you to the next cell. Your mission? Make at least one valid path from the top-left corner to the bottom-right corner, but it might cost you! Each time you change a sign, you incur a cost of 1. Our goal is to minimize this cost to create a valid path. Let’s dive into the problem, explore a solution, and dissect it step-by-step! 🤓

Bitwise operations often feel like solving puzzles 🤔, and the “Minimize XOR” problem brings an intriguing challenge to the table. This problem combines set bits, XOR operations, and some clever decision-making to find a unique solution. Ready to dive into this binary journey? Let’s decode it together! 🖥️🔍

Imagine you’re tasked with creating good strings 🧵 from two components (zero and one), while keeping their lengths within a range (low and high). The goal is to calculate how many such strings you can generate. This problem combines recursion, memoization, and creativity in dynamic programming. Let’s dive deep into this fascinating challenge! 🚀

Let’s dive into an exciting problem that challenges our understanding of dynamic programming! 🚀 We’re tasked with figuring out the number of ways to form a target string using characters from a list of strings, under some specific constraints. Ready to explore this fascinating challenge? Let’s go! 🎉

Ever planned a sightseeing tour and wondered which two spots would maximize your experience? While real-life planning can be tricky, solving this problem algorithmically is super fun and insightful! Today, we’ll dive deep into a fascinating problem where we aim to find the best sightseeing pair. Get ready for a journey full of numbers, logic, and, of course, some fun examples! 🚀

Binary trees are fascinating data structures, and problems like this one help us understand their traversal techniques while solving practical challenges. Let’s dive into this exciting problem and explore how to find the largest value in each row of a binary tree! 🚀

Imagine you have two trees 🛠️ and need to merge them by connecting one node from each tree with a single edge. Your goal? Minimize the diameter of the resulting tree! 🌟

Sorting a binary tree by level values can be tricky, but with a few clever swaps, it’s achievable! Let’s dive into an exciting problem where we aim to minimize the number of operations required to make the values at each level sorted in increasing order. Along the way, we’ll explore a neat mix of tree traversal and array sorting techniques!

Imagine you’re in charge of a school with several classes preparing for final exams. You have a group of extra brilliant students ready to boost any class’s pass rate. The challenge? Assign them strategically to maximize the average pass ratio. Let’s dive into this problem and solve it using a mix of greedy and heap-based techniques! 🕵️✨

Welcome back to another LeetCode adventure! Today, we’re solving a fascinating problem involving marking elements in an array, updating their neighbors, and calculating the score. This problem is both an exercise in logical thinking and a great opportunity to practice working with priority queues. Let’s dive in!

Have you ever wondered how to make an array as beautiful as possible by adjusting its values? 😍 This problem is all about maximizing the beauty of an array by tweaking its elements. Let’s dive into an intriguing challenge that combines sorting and sliding window techniques for a highly efficient solution! ✨

When juggling multiple opportunities, the trick lies in choosing the ones that offer the highest rewards while avoiding overlaps. This problem is a classic example of finding an optimal solution under constraints, where we aim to maximize the sum of values by attending at most two non-overlapping events. 💡 Let’s unravel it step by step and dive into the magic of efficient algorithms! 🚀

Let’s dive into an exciting problem from LeetCode: 1760. Minimum Limit of Balls in a Bag. 🚀 We’ll explore the problem, break it into manageable parts, and tackle it step by step. Get ready for some problem-solving action with detailed explanations, walkthroughs, and Python code snippets! 🐍✨

In this blog post, we’re tackling a fun and challenging problem: selecting integers while respecting some tricky rules. It’s like solving a puzzle where every piece must fit perfectly. 🤓 Let’s dive in and break it down step by step! 💪

Transforming one string into another through clever moves is a fascinating challenge. In this problem, we deal with moving characters L and R on a grid filled with blanks _ while adhering to strict movement rules. Can we turn the string start into target? Let’s find out! 🚀

When you’re handed a couple of strings and a bit of magic in the form of cyclic increments, can you make one a subsequence of the other? This problem combines creativity, pointer tricks, and some smart thinking, and it’s bound to test your problem-solving skills. Let’s break it down in this in-depth guide! 🎉

In this post, we’ll dive into LeetCode Problem #2109: Adding Spaces to a String. This problem involves manipulating strings based on a set of indices, making it a fantastic opportunity to brush up on string slicing and iteration. We’ll explore the problem step-by-step, tackle it with an optimized solution, and showcase an example walkthrough using higher numbers for clarity. Let’s get started! 🌟

Given a sentence and a search word, determine if the search word appears as a prefix in any word of the sentence. If it does, return the 1-indexed position of the first word that matches. Otherwise, return -1.

In this blog post, we’ll explore LeetCode problem #1346: Check If N and Its Double Exist. This problem challenges us to think critically about array manipulation and efficient lookups. We’ll take a deep dive into solving this problem with a detailed explanation, optimized solutions, and an in-depth example walkthrough. Plus, there will be plenty of emojis to keep it engaging! Let’s get started. 🚀

Imagine arranging puzzle pieces 🔄, where each piece has a start 🟩 and an end 🟥. The challenge is to arrange them so the end of one piece perfectly matches the start of the next. Let’s dive in and untangle this challenge using Eulerian paths in graph theory! 🚀

Imagine you are navigating a grid 🚦. Each cell in this grid has a “minimum unlock time,” meaning you can only visit it after waiting a certain amount of time⏳. Starting from the top-left cell, your goal is to reach the bottom-right cell as quickly as possible while adhering to the timing rules. If no valid path exists, return -1.

In this blog post, we’ll tackle LeetCode #2290: Minimum Obstacle Removal to Reach Corner, an exciting graph-based problem that tests our algorithmic skills! We’ll go step-by-step, exploring the problem statement, understanding the examples, diving into solutions, and optimizing our approach. Let’s remove those obstacles and get coding! 💪✨

Are you ready to streamline your data workflows by connecting Bitbucket with Dataform? Let’s dive into this guide and make your data integration journey smoother than ever. 🚀

Finding the shortest path in a graph is one of the foundational problems in computer science, especially in applications like routing, logistics, and network optimization. Today, we’ll explore an intriguing variation of the shortest path problem where the graph evolves over time, and we compute the shortest path dynamically! This problem is a medium-difficulty gem from LeetCode: Shortest Distance After Road Addition Queries. Buckle up! 🚀

Imagine a thrilling tournament 🏟️ with a unique structure—a Directed Acyclic Graph (DAG)! Each team battles it out, and your goal is to crown the true champion 🥇. The twist? The champion must be the strongest team with no other team stronger than it. If there’s any ambiguity, the title of champion is denied! Let’s dive into this exciting problem!

Welcome to a comprehensive deep dive into LeetCode Problem #773: Sliding Puzzle. This problem combines elements of graph traversal, logical deduction, and algorithmic problem-solving. It’s a must-know challenge for any aspiring software engineer or coding enthusiast looking to level up their BFS skills. Buckle up as we unravel every detail of this exciting problem, with examples, code walkthroughs, and advanced insights to help you ace it! 💪

Have you ever worked with numbers in a matrix and wondered how to manipulate them for maximum value? 💭 This problem lets us explore a fascinating technique where flipping the signs of adjacent numbers helps us maximize the total matrix sum. Intriguing, right? Let’s dive into the mechanics, supported by plenty of explanations, examples, and edge cases to make sure you master it! 🚀✨

Are you ready to tackle a fun matrix problem that combines the effects of gravity and rotation? This problem is a mix of geometry and simulation, guaranteed to get your problem-solving gears turning! Imagine stones falling through empty spaces, obstacles blocking their path, and the whole box spinning 90° clockwise. Let’s dive into this visual and engaging challenge! 🚀

This problem takes us into the world of binary matrices and challenges us to flip columns to make rows identical. Using the power of Python and some creative pattern recognition, we can solve this efficiently. Let’s dive into the solution step by step! 🌟

Have you ever wondered how to ensure maximum coverage with minimal resources, like setting up surveillance cameras in a building or guarding the boundaries of a field? 🏢🔍 While it might seem like a logistics puzzle, problems like these often have practical solutions grounded in computational thinking. One such fascinating challenge is LeetCode Problem #2257: Count Unguarded Cells in the Grid. 🧩👀

Dealing with constraints in strings can feel like a balancing act! 🧩 In LeetCode problem #2516: Take K of Each Character From Left and Right, the challenge is to efficiently remove characters from the edges of a string while ensuring at least k occurrences of 'a', 'b', and 'c' remain. Using the sliding window technique, we’ll determine the minimum time required to achieve this. Ready to dive into a smart and efficient solution? Let’s go! 🚀

When we think about finding maximum subarray sums, things can get tricky when there’s a twist: all elements in the subarray must be distinct! 🤯 This problem brings an exciting challenge, combining sliding window techniques and a hint of hash magic 🪄 to create a solution that’s efficient and clean.

In a world filled with codes and secrets, you are the chosen one to defuse the bomb! 🕵️‍♂️💣 Your informer has passed you a circular array of numbers and a mysterious key. Can you decode it and save the day? 🌍🔥

Bayes’ Theorem is a mathematical framework that allows us to update probabilities based on new information. It plays a crucial role in finance, where decision-making under uncertainty is the norm. Whether it’s assessing credit risk, predicting market movements, or identifying fraudulent transactions, Bayes’ Theorem equips professionals with a robust tool to make informed choices.

Finding the shortest subarray with a sum of at least \(k\) is a tricky but highly rewarding challenge. This problem merges concepts like prefix sums, sliding windows, and deques, making it an absolute gem for algorithm enthusiasts! Let’s solve it step by step while keeping it fun and engaging with loads of examples and insights! 🌟

Welcome to another exciting blog post! 🚀 In this problem, we’re exploring the power of subarrays. It’s all about finding whether elements are sorted and consecutive while dealing with dynamic arrays of size k. Along the way, you’ll enhance your problem-solving skills and coding mastery! Let’s jump into it! 💻🤓

Sorting arrays is a common requirement in computer science and forms the basis for many efficient algorithms. But what if you’re given an array that’s almost sorted? In this problem, we need to remove the shortest contiguous subarray to transform the remaining elements into a sorted (non-decreasing) sequence.

Distributing products to stores efficiently is no easy task, especially when aiming to keep each store’s load as balanced as possible! In this problem, we’re challenged with minimizing the maximum products any one store gets. By carefully using a combination of binary search and validation techniques, we can solve this seemingly complex problem with ease!

Counting pairs 🧮 in an array that meet specific conditions can be quite tricky—especially when large numbers are involved! In this post, we’ll dive into finding fair pairs where the sum of two elements falls within a given range. With sorted arrays and two-pointer techniques, we’ll show how to do this efficiently!

Imagine you’re in a store filled with beautiful items, each with a price and a beauty score. You have a limited budget for each shopping trip, and you want to get the most beautiful item that your money can buy! This problem is all about finding the maximum beauty within a price constraint for each query. Let’s explore how we can efficiently solve this with Python!

Imagine you’re given an array of numbers, and you need to make it strictly increasing by subtracting prime numbers from each element. Sounds like a math puzzle, right? 🤔 Let’s dive into how we can achieve this efficiently using an optimized approach with prime numbers! 🌟

Given an array of non-negative integers, we aim to find the smallest subarray where the bitwise OR of all elements is at least a given integer k. This problem involves bit manipulation and sliding window techniques, which are essential for efficient solutions!

Today, we’ll dive into an interesting problem where we have to construct a strictly increasing array with a given length n, ensuring that a bitwise AND operation across all elements results in a specific number x. Our goal? To keep the last element of this array—the largest one—as small as possible! 🚀 Let’s break down the problem, understand the logic, and see how Python code helps us solve it efficiently! 🐍💡

Hey, coding enthusiasts! 😃 Let’s dive into a unique problem that brings together bitwise manipulation and array queries. XOR operations are at the core of this challenge, and with a little understanding of binary numbers, we can quickly see how to solve it effectively. This post will not only explain the problem but will also show you the power of bitwise tricks in competitive programming. Let’s get started!

In this problem, we’re exploring bitwise operations, particularly the bitwise AND operation. Our goal is to find the largest subset of numbers from a list such that the bitwise AND of all numbers in the subset is greater than zero. 🔥

Welcome back, coders! Today, we’re tackling an exciting array problem that tests our understanding of sorting with constraints. We’ll explore how to determine if an array can be sorted by swapping elements with the same number of set bits in their binary representation. Let’s dive in! 🏄‍♂️

Binary strings are at the core of many computer science problems. Today, we’re going to dive into transforming a binary string into a beautiful one. What does it mean to make a binary string “beautiful”? Here, it’s all about structuring the string so that it can be split into even-length sections containing only 0s or only 1s. This problem is all about finding the minimum number of changes required to achieve that “beautiful” transformation.

When working with large strings, especially those filled with repeating characters, compression can save valuable space. In LeetCode problem #3163: String Compression III, we’re asked to create a compressed version of a string while following a specific rule: we can capture only up to 9 consecutive characters of the same type at a time. This twist challenges us to think carefully about how to segment the string efficiently without missing any repetitions.

Imagine you have two strings, s and goal, and you want to know if you can rotate s some number of times so it matches goal. Let’s dive into how to solve this with some cool insights and a clean algorithm! 🌐✨

In today’s post, we’re tackling a delightful problem that asks us to determine if a sentence is circular. Imagine each word is linked, like a chain, by matching the last letter of each word with the first letter of the next. Sounds easy, right? Well, let’s dig into the details and explore both a straightforward solution and a more optimized approach. Let’s get started! 💥

Imagine a long string with repeating characters that looks a bit cluttered. For instance, “aaabaaaa” has groups of consecutive characters that make it challenging to read. Our mission is to refine this string to make it look fancy! In a fancy string, no three consecutive characters should be the same. 🪄 By removing the minimum number of characters, we’ll transform the input string to meet this condition. Let’s dive into the details of how to approach this problem and implement an efficient solution.

Imagine you’re managing a repair line where broken robots 🛠️ keep moving along a straight road to reach their assigned factories 🏭. The goal? To minimize the total distance that all the robots need to travel to get repaired! This problem is challenging, but by breaking it down, you can use Dynamic Programming (DP) to achieve an optimal solution. Let’s get into it! 🧑‍💻👇

With technological and societal shifts shaping the landscape of the future, certain industries stand out as especially promising for long-term investment. Today, we’re seeing exponential growth in fields that tackle major global challenges, from environmental sustainability to health innovation. Here, we’ll dive into some of the most exciting industries that could lead the way in the coming years—and yes, Artificial Intelligence (AI) is one of them.

Creating a mountain array from an unordered sequence of numbers might seem complex at first, but the problem is ultimately about finding an efficient way to keep the “mountain” structure without losing too many elements. 🌄 This challenge boils down to identifying the longest possible sequence that forms a mountain and then calculating how many elements we need to remove to achieve this structure. This type of problem is a great exercise in applying dynamic programming techniques, as we’ll need to store and reuse values to optimize our solution.

In this problem, we’re dealing with a matrix of numbers, representing a grid where each number is a cell. Our task is to start from any cell in the first column and traverse rightwards across the grid, only moving to cells with a higher value than our current cell. The goal? To find the maximum number of moves we can make in such a path! Let’s explore how to achieve this optimally using Dynamic Programming (DP). 🤩

In this problem, we’re hunting for streaks of squares within a sequence of numbers! Imagine a magical sequence where each number is the square of the one before it—our task is to find the longest such sequence (or confirm if no such streak exists). Let’s dive into the details and explore a step-by-step approach to tackle it! 🧩

Counting square submatrices with all ones in a binary matrix challenges our understanding of matrix traversal and dynamic programming! In this problem, we want to find how many square submatrices consist entirely of ones. We’ll explore a brute-force solution, understand its limitations, and then optimize it with dynamic programming. Ready to uncover all the squares? Let’s dive in! 🎉🔍

Hello, algorithm lovers! 👋 Today, we’re going to solve a fascinating problem that involves removing subtrees from a binary tree and calculating the tree’s height afterward. The challenge in this problem lies in managing multiple independent queries efficiently. By the end, we’ll be equipped with a fast, optimized solution that can handle even the most extensive trees! Let’s dive in! 🚀

Managing files can be a hassle, especially when we want to clean up our directory structure! This problem is all about identifying and removing redundant sub-folders in a list. Let’s explore the problem and find out how to tackle it efficiently. 😊

Completing this thesis was a journey that took longer than anticipated. While deeply invested in my work as a Software Engineer & Data Scientist, I often found it challenging to fully focus on my research. This thesis became somewhat of a side quest, a project that I worked on whenever I could spare the time from my demanding job. The challenge of balancing professional responsibilities with academic work required discipline and perseverance.

Welcome back, coders! 👨‍💻👩‍💻 Today, we’re going to dive deep into a fascinating problem that involves binary trees, recursion, and some nifty tree manipulations – specifically, flip operations! 🌲🔀🌲 In this post, we’ll learn how to check if two binary trees are flip equivalent, meaning that by flipping some nodes’ children, we can make one tree look like the other. 🌀

In this problem, we’re tasked with modifying a binary tree 🌲 by replacing the value of each node with the sum of all its cousins’ values. Two nodes are considered cousins if they are at the same level but have different parents. This problem requires us to explore tree traversal techniques and depth-based operations, making it an exciting challenge for those familiar with binary trees and recursive algorithms! 😎✨

In this post, we’ll tackle an exciting problem: finding the Kth largest level sum in a binary tree. 🌲 Each level of a binary tree contains nodes with specific values, and our goal is to compute the sum of these nodes for every level, then pick out the Kth largest sum. Whether you’re into BFS, DFS, or both, you’ll love diving deep into this one! 😎

In this problem, we are tasked with maximizing the number of unique substrings into which we can split a given string. The key is to ensure that no substring appears more than once. We can explore different ways to split the string, but we must always guarantee that the resulting substrings are distinct. Let’s walk through this challenge with plenty of examples and explore the approach that will help us crack this problem efficiently using backtracking!

This problem revolves around evaluating complex boolean expressions that include logical operators like AND (&), OR (|), and NOT (!). Given a valid string representing a boolean expression, our goal is to parse it and determine whether it evaluates to true or false. This problem involves string parsing, recursion, and logical operations, making it a fun and challenging puzzle!

In this problem, we explore a fascinating sequence of binary strings where each new string is built recursively by combining the previous string, a “1”, and the reversed and inverted version of the previous string. Our goal is to efficiently determine the kth bit of the nth binary string, without explicitly constructing the entire string.

Ever wanted to unlock the maximum power from an array using just bitwise operations? 💡 Well, today’s problem gives us exactly that opportunity! In this exciting challenge, we need to find the maximum possible bitwise OR from all the non-empty subsets of an array, and count how many subsets can achieve that value! 🚀

Ever wonder how swapping two digits can make a huge difference? Here’s how to do it efficiently!

In this post, we’re going to explore how to construct a Quad-Tree from a given n x n grid filled with 0’s and 1’s. A Quad-Tree is a tree data structure that is particularly useful for partitioning a two-dimensional space.

In today’s problem, we need to create the longest happy string using three letters: 'a', 'b', and 'c'. A happy string avoids repeating any letter more than twice in a row. The challenge lies in constructing the longest string while respecting this rule, and we’ll explore both a basic approach and an optimized approach!

In this problem, you’re tasked with grouping black ⚫ and white ⚪ balls represented as a binary string. The goal is to move all the black balls to the right side and all the white balls to the left side with the minimum number of adjacent swaps.

In this problem, we are tasked with maximizing the score by applying operations to an array. The goal is to select the maximum possible element, increase the score, and then replace it with its ceiling after dividing by 3. Let’s break this down and find an efficient approach! 🚀

Artificial Intelligence (AI) has become the hottest buzzword in the tech world. Businesses and startups are clamoring to integrate AI into their operations, hoping to ride the wave of innovation and attract the attention of investors. The common belief is that by injecting AI into a project, it automatically becomes more attractive, more efficient, and more profitable. But from a Venture Capital (VC) perspective, this obsession with AI is leading many businesses astray, and investors are increasingly being misled by the allure of AI-powered promises.

In this post, we’ll tackle a hard problem from LeetCode: Smallest Range Covering Elements from K Lists. The challenge is to find the smallest range that includes at least one number from each of the given k lists of sorted integers. This problem is a fantastic way to practice using heaps or a sliding window technique with pointers.

In this problem, we are tasked with dividing a set of intervals into groups such that no two intervals in the same group overlap. Each group should be able to hold non-overlapping intervals, and our objective is to minimize the number of groups required. This problem is about efficiently managing the overlap of intervals and finding the minimum number of distinct groups to separate them.

Now that we’ve gone through the theory and some optimizations in the previous parts of this blog series, it’s time to put everything together and build a complete Large Language Model (LLM) from scratch. If you haven’t already, I encourage you to check out the earlier posts in this series to understand the foundational concepts and techniques:

Absolutely! Let’s dive into Part 3, where we’ll cover advanced optimization techniques, explore the use of Transformer models in real-world tasks, and discuss how to fine-tune these models for improved performance. This part will aim for around 7000 words, delving deeply into topics like advanced training strategies, improving performance, regularization techniques, and practical applications. This will round out the entire process of building and training Transformer models from scratch.

In Part 1, we discussed the foundational concepts behind ChatGPT and language models. We explored how they work, what powers them, and why they are essential for Natural Language Processing (NLP). We also tokenized the Tiny Shakespeare dataset and laid the groundwork for building our own language model.

Hey everyone! 👋 Have you ever wondered what powers ChatGPT? It’s the technology behind some of the most fascinating AI conversations today, and it has captivated the world with its seemingly human-like responses. But how exactly does it work? 🤔 Why is it so good at understanding and generating text? 🚀

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) 🌍. These models, built on deep learning techniques, power everything from chatbots to recommendation systems to AI assistants. But what makes them so powerful? Let’s dive deep into their architectures 🛠️.

Ever wondered how to find a seat at a party, especially when it’s a race for the smallest unoccupied chair? This fun problem dives into seating friends at a party and ensuring we track who sits where — and more importantly, where your friend sits! Let’s solve it step by step.

In this post, we’ll explore a fun and interesting problem where we need to find the maximum width ramp in an array. A ramp is defined as a pair of indices (i, j) such that i < j and nums[i] <= nums[j]. Our goal is to calculate the largest difference j - i where this condition holds. Let’s dive in! 🌊

Have you ever encountered a string of parentheses that looks a bit off? The challenge here is to make sure every opening parenthesis ( has a matching closing parenthesis ). We’re tasked with calculating the minimum number of insertions needed to make the string valid. 🚀

Balancing brackets is a common problem in programming interviews, and in this challenge, we are tasked with making a string of brackets balanced with the minimum number of swaps. Given that the string has an equal number of opening ([) and closing (]) brackets, the goal is to determine the least number of swaps required to make it balanced.

Given a string consisting of only uppercase English letters, we need to repeatedly remove the substrings “AB” and “CD” until no more of them exist. Our task is to find the minimum possible length of the string after all these operations.

In competitive programming and real-world applications, sentence manipulation and comparison are common tasks. Whether you’re working on natural language processing (NLP), chatbots, or search engines, understanding how to determine sentence similarity is crucial.

Markov Chains are powerful models that help predict future events based on current conditions. In the world of finance, they are widely used to model market states such as bull markets 📈, bear markets 📉, and stable markets 📊. But how do we calculate these transitions, and what are the key factors to keep in mind? In this blog, we will explore the mathematics behind it, the factors that influence these calculations, and a real-world financial example.

You’re given two strings s1 and s2. The goal is to check if s2 contains a permutation of s1. In other words, you want to return True if any permutation of s1 exists as a substring in s2, and False otherwise. The challenge lies in optimizing this process! 🚀

In competitive games, forming balanced teams is essential to ensure fair play. Imagine you’re tasked with dividing players into teams where each team has the same combined skill level. Not only that, but the chemistry of each team is calculated as the product of their individual skills. Sounds like a challenge, right? Let’s break down the problem and find a solution step by step!

Markov Chains 🔗 are an essential mathematical concept used in various fields, including finance. This guide will take you from the basic definition of Markov Chains to their sophisticated applications in finance, using formal mathematical definitions and real-world examples.

In this blog post, we’ll solve an interesting problem where the goal is to remove the smallest subarray from a list of integers such that the remaining sum is divisible by a given number, p. We’ll explore both the problem and its optimized solution using a hash map.

Today, I had the pleasure of attending the Google Cloud Summit in Vienna, a day packed with insights, networking, and hands-on experiences focused on AI and Cloud technologies. The event, titled “Lernen Sie die Vorteile von KI und Cloud kennen”, was all about discovering the benefits of AI and how Google Cloud can empower businesses to tackle their challenges head-on and stay ahead in the competitive landscape.

Hey there, coding enthusiasts! 👋 Today, we’re going to tackle a simple yet interesting problem: ranking the elements of an array based on their values. Sounds fun, right? Let’s dive in! 🚀

Hello, problem solvers! 👋 Today, we’re going to tackle a cool problem that involves dividing an array into pairs, ensuring the sum of each pair is divisible by a given number k. Let’s explore how to solve it efficiently! 🚀

Hey 😄 Today, we’ll be solving an interesting stack problem: designing a stack that supports increment operations in addition to the usual push and pop. Ready? Let’s dive in!

Welcome back to the quant corner! Today, we’re diving deep into the Finite Difference Methods (FDM) and their indispensable role in financial engineering. Whether you’re a budding quant or a seasoned financial analyst, understanding FDM is crucial for pricing complex financial derivatives and managing risk effectively. Let’s unravel the mathematics behind it and see a real-world application using Python. 🚀

Monte Carlo simulation is one of quantitative finance’s most widely used tools. It’s a probabilistic method for modeling uncertainty and simulating various outcomes. The core idea is to generate random samples to understand the potential future performance of financial products like stocks, options, and portfolios.

Hello, fellow coders! 👋 Today, we’re diving into a powerful data structure challenge: implementing an All O(1) Data Structure. The goal is to support operations to manage and query key counts efficiently, all in constant time O(1). Let’s break it down step by step! 🧠

Hey there! 🎉 Today, we’re diving into an exciting problem: designing a Circular Deque (double-ended queue) from scratch! The challenge requires implementing the deque and its fundamental operations like insertion, deletion, and fetching elements from the front or rear in a circular manner.

Mastering SQL is a crucial skill for data science interviews. Today, we’re diving into three SQL challenges from DataLemur. These problems not only help you sharpen your SQL skills but also give you practical insights into real-world data science scenarios. Let’s explore the solutions! 🚀

In this post, we will tackle the problem of implementing a calendar that allows booking events without causing a triple booking. After exploring a basic solution, we’ll introduce an optimized solution using a sweep line algorithm to improve efficiency for large datasets. Let’s dive in!

In finance, we’re often overwhelmed by the sheer number of variables we need to track. Whether it’s stock returns, risk indicators, or macroeconomic factors, the amount of data can be daunting. Principal Component Analysis (PCA) is a powerful tool that helps us reduce this complexity by transforming correlated variables into a smaller set of uncorrelated ones, without losing essential information.

If you’ve ever found yourself endlessly scrolling through social media, binge-watching shows, or frequently using an app, you’ve experienced the power of habit-forming products. But how do companies build these addictive experiences? 🤔

In this post, we will tackle the problem of implementing a simple calendar that allows booking events without causing a double booking. After exploring a basic solution, we’ll introduce an optimized solution using a balanced binary search tree (BST) to improve efficiency for large datasets. Let’s dive in!

In the world of NLP (Natural Language Processing), general models like GPT-3 and PaLM have been leading the way with their versatility in tasks ranging from answering questions to generating creative text. But what about specialized domains like Finance? Enter BloombergGPT — a groundbreaking language model designed specifically to tackle the complexities of the financial industry. 🌍

In a previous post, I introduced the fundamental concepts of eigenvalues and eigenvectors. Today, we’re going to dive deeper into how these mathematical tools are applied in finance, particularly for portfolio optimization, risk management, and stress testing. Eigenvectors help us uncover hidden relationships between assets and risks that are otherwise difficult to see using traditional methods. We’ll illustrate this with real-world data, mathematical rigor, and a focus on practical applications. Let’s begin our journey into eigenvectors and financial analysis! 📊

In this post, we will tackle the problem of finding the sum of prefix scores of strings. We’ll explore both a brute-force approach and an optimized solution using a HashMap to solve this problem efficiently. Let’s dive into it!

The balance sheet is one of the three primary financial statements used to evaluate a company’s financial position, along with the income statement and cash flow statement. It provides a snapshot of a company’s assets, liabilities, and equity at a specific point in time, helping stakeholders assess the company’s stability, liquidity, and capital structure. Let’s explore the key components and terminology associated with the balance sheet in detail! 💼

In the early stages of any company, regardless of size or industry, data can be both an asset and a liability. As teams focus on growth, product development, and customer acquisition, data management often gets pushed aside—leading to headaches down the road.

When working with arrays of integers, you may encounter the task of finding the length of the longest common prefix between all pairs of integers, one from each array. In this post, we’ll explore both a brute-force approach and a more optimized solution to solve this problem efficiently.

Matrices, Eigenvalues, and Singular Value Decomposition (SVD) are fundamental concepts in linear algebra, with applications ranging from data science to machine learning, physics, finance, and beyond. In this post, we’ll break down these topics in a beginner-friendly way, explaining what they are, how they work, and why they’re important. By the end, you’ll have a solid foundation to build on for more advanced topics. 🎓

Hello, and welcome to my blog! 🎉 I’m excited to take you through my life journey, not just the professional side of things but also the passions and projects that keep me motivated outside of work. 🌱