Outcome School

What Each Transformer Block Learns? LLMs learn in layers inside the Transformer: We have N Transformer blocks, stacked one after another. Assume N = 12. What each Transformer block learns: • Block 1 to 4: basic patterns (tokens, positions, simple relations) • Block 5–8: syntax (phrases, grammar) • Block 9-12: semantics (meaning, long-range context) Just like reading a book: letters → words → sentences → meaning. Humans can’t reverse this process while reading. A similar thing happens in this architecture, and this is an emergent behavior, not something explicitly hard-coded. This happens because of: • Stacked composition: The model learns by building ideas step by step, layer on top of layer. • Residual learning: Each layer adds a small improvement without forgetting what was already learned. Understanding emerges layer by layer. This is what each Transformer block learns. #llm #ai #deeplearning

Learn about JvmStatic annotation #AndroidDev #Kotlin #Java

10 Kotlin-related Android Interview Questions Questions: • What is an inline function in Kotlin? • What is the advantage of using const in Kotlin? • lateinit vs lazy in Kotlin • open keyword in Kotlin • internal visibility modifier in Kotlin • init block in Kotlin • JvmStatic, JvmField, JvmOverloads Annotation • What is a reified keyword in Kotlin? • Suspending vs Blocking in Kotlin Coroutines • Launch vs Async in Kotlin Coroutines Answers: https://lnkd.in/gUXqXcAv #AndroidDev #Kotlin #interview

When to use Dispatchers . Unconfined? As per the official documentation: "A coroutine dispatcher that is not confined to any specific thread. It executes the initial continuation of a coroutine in the current call frame and lets the coroutine resume in whatever thread that is used by the corresponding suspending function, without mandating any specific threading policy." It does not change the thread. When it is started, it runs on the thread on which it was started. If it is resumed, it runs on the thread that resumed it. We should use Dispatchers . Unconfined when we do not care where the coroutine will be executed. launch(Dispatchers . Unconfined) { // Your task for which you do not care about the thread on which it should run. } Example use case: Unit Testing as Dispatchers . Unconfined does not change the thread. This was all about the Dispatchers . Unconfined in Kotlin Coroutines. #AndroidDev #Kotlin

Implement Machine Learning algorithms from scratch, each one helps you learn these key concepts: • Linear Regression: loss functions, gradients, and optimization. • Logistic Regression: probabilities and cross-entropy loss. • K-Nearest Neighbors (KNN): distance metrics, feature scaling, and similarity-based learning. • Decision Tree: entropy, information gain, and non-linear decision rules. • Naive Bayes: Bayes’ theorem and conditional probability. • Support Vector Machine (SVM): margin maximization, regularization, and kernel methods. • K-Means Clustering: unsupervised learning, centroid updates, and iterative optimization. #AI #MachineLearning

I just published a blog: Causal Masking in Attention - Intro: What is causal masking? - Problem: Seeing future tokens - Solution: Causal masking - Implementation: Masked attention matrix - Result: Zero attention to future tokens Read here: https://lnkd.in/gZqKcCgk #machinelearning #llm #deeplearning #ai #transformers

Designing an Offline-First System in Android: A frequently asked interview question in Android System Design Rounds. • Local source of truth: Store data locally (SQLite) • Sync engine: Sync with the server whenever the network is available. • Conflict resolution and Versioning: Conflicts can occur if the same user updates the same data from another device while offline. Implement conflict resolution strategies (last-write-wins, merge, or ask user). • Delta Sync: Only sync changes rather than overall data. • Network State Monitoring: Use ConnectivityManager to detect when the device goes online. Keep Learning, Keep Sharing, and Keep Growing. #AndroidDev #Kotlin #Android

What residual (skip) connections do in Transformers In a Transformer block, you'll always see patterns like: x = x + f(x) That is the residual connection. It allows the model to learn a refinement on top of the input, rather than learning everything from scratch. So, the residual connections let layers add knowledge instead of replacing it. Let's try to understand with an analogy. Imagine you are learning a topic over a period of time. Without a residual (skip) connection: x = f(x) • Whatever you learn each day, you REWRITE your notes from scratch. • Old knowledge is NOT preserved. With a residual (skip) connection: x = x + f(x) • You keep your existing notes and only add what is useful from today's learning. • Old knowledge is preserved. • If today's lesson isn't useful, you add nothing. This is what residual (skip) connections do in Transformers. Keep Learning, Keep Sharing, and Keep Growing. #machinelearning #llm #deeplearning #ai #transformers

CI/CD Pipeline for Android #AndroidDev

CoroutineContext is a key part of Kotlin coroutines, but it can be confusing. In this blog, we will learn about the CoroutineContext in Kotlin by going through the source code. We will also cover how to customize it. Read here: https://lnkd.in/gaq76-wD #AndroidDev #Kotlin