A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.

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Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.

On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.

With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.

Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is Max Norm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐜𝐨𝐮𝐫𝐬𝐞 : https://www.edureka.co/microse....rvices-architecture- . Use Code(𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
Do you know the main difference between microservices and monolithic architecture?
Then this video is for you, I share when you would use each of these in practice.The guide discusses the topic of monolithic vs microservices architecture, including a list of pros and cons to each option.
Introduction:
01:07 What is monolithic?
02:06 Monolithic Architecture
03:47 What are microservices?
04:41 Microservices Architecture
06:08 Why to migrate from Monolith to Microservices
08:21 Monolith vs Microservices

📝Feel free to comment your doubts in the comment section below, and we will be happy to answer📝

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Is there any eligibility criteria for this program?
A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

About the course :
Why learn Microservices?
Microservices structures an application as a collection of small autonomous services, modeled around a business domain. It uses minimal resources while reducing the cost of ownership. Also, it is easy to build and maintain applications with this architecture. With these added advantages many companies are looking to move to Microservices. Getting certified can help you aim for better job opportunities.

Can I Attend a Demo Session before Enrolment?
If you have seen any of our sample class recordings, you don't need to look further. Enrollment is a commitment between you and us where you promise to be a good learner and we promise to provide you the best ecosystem possible for learning. Our sessions are a significant part of your learning, standing on the pillars of learned and helpful instructors, dedicated Personal Learning Managers, and interactions with your peers.

Is the course material accessible to the students even after the course training is over?
Yes, access to the course material will be available for a lifetime once you have enrolled in the course.

🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐌𝐲𝐒𝐐𝐋 𝐃𝐁𝐀 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/mysql-dba (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
A transaction is the propagation of one or more changes to the database. For example, if you are creating a record or updating a record, or deleting a record from the table, then you are performing transactions on the table. It is important to control transactions to ensure data integrity and to handle
database errors. Practically, you will club many SQL queries into a group and you will execute all of them together as a part of a transaction. this video helps you to learn about transactions and transaction manipulation.
Topics Covered :
00:00:00 Introduction
00:00:22 Transactions
00:01:34 Properties
00:03:16 COMMIT & ROLLBACK
00:03:51 AUTO-COMMIT
00:04:41 Transaction- Safe Table Types in MySQL
00:06:12 ALTER command & syntax

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Welcome to this comprehensive SQL tutorial course. This course covers the basics of relational databases and SQL, including setting up MySQL, inserting data, and working with aggregation, grouping, and pagination in SQL queries. It also covers advanced topics such as combining tables using joins, executing SQL queries using Python and SQL Alchemy, and solving technical interview questions. By the end of this course, you'll have the knowledge and confidence to excel in SQL.

✏️ Course created by@jovianhq

Important Links
🔗 Relational Databases notebook - https://jovian.com/aakashns/re....lational-databases-a
🔗 Advanced SQL Aggregation & Joins notebook - https://jovian.com/aakashns/ad....vanced-sql-aggregati
🔗 SQL data file: https://raw.githubusercontent.....com/harsha547/Classi

A database is an organized collection of structured information, typically stored in the form of tables (rows & columns). Relational databases allow storing and retrieving different kinds of related information e.g. products, customers, and orders for an online shopping site. Structured Query Language or SQL (pronounced "sequel") is the most widely used language for interacting with relational databases, and is an essential skill for Data Science professionals.

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:28) Relational Databases & SQL
⌨️ (0:03:05) Setting up MySQL
⌨️ (0:29:23) Inserting Data into the Table
⌨️ (0:56:32) Practice Exercises
⌨️ (1:22:12) Aggregation, grouping & pagination in SQL queries
⌨️ (2:23:17) Mapping and Arithmetic Functions
⌨️ (2:54:53) Working with Dates
⌨️ (3:12:16) Combining Tables using Joins
⌨️ (3:30:14) Executing SQL queries using Python and SQL Alchemy
⌨️ (4:11:06) 3 step approach to Interview Questions
⌨️ (4:36:58) Interview Q - Apple
⌨️ (4:38:59) Interview Q - Linkedin
⌨️ (4:43:09) Interview Q - Meta
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⌨️ (4:59:18) Interview Q - Amazon
⌨️ (5:07:27) Interview Q - Google

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Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.

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Learn about vector embeddings and how to use them in your machine learning and artificial intelligence projects. Learn how to create an AI assistant with vector embeddings. You'll use OpenAI's GPT-4 API, LangChain, and Natural Language Processing techniques (NLP).

✏️ Course created by @AniaKubow

⭐️ Contents ⭐️
⌨️ (00:27) Introduction
⌨️ (01:49) What are vector embeddings?
⌨️ (02:14) Text embeddings
⌨️ (07:58) What are vector embeddings used for?
⌨️ (11:05) How to generate our own text embedding with OpenAI
⌨️ (14:37) Vectors and databases
⌨️ (16:02) Getting our database set up
⌨️ (18:05) Langchain
⌨️ (19:24) Let’s build an Ai Assistant

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Read hundreds of articles on programming: https://freecodecamp.org/news

This is the 1st video in a series on using large language models (LLMs) in practice. I introduce LLMs and three levels of working with them.

▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos
📰 Read more: https://towardsdatascience.com..../a-practical-introdu

Music by Me
https://soundcloud.com/shawhin....-talebi/letsalljustt
https://www.youtube.com/@shawhin-music

--
Homepage: https://shawhintalebi.com/

Intro - 0:00
What is an LLM? - 1:13
Zero-shot Learning - 3:36
How do LLMs work? - 5:44
3 Levels of Using LLMs - 7:52
Level 1: Prompt Engineering - 8:22
Level 2: Model Fine-tuning - 11:00
Level 3: Build your own - 13:13

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Welcome to our in-depth tutorial on Ethical HackerGPT! In this video, we'll explore HackerGPT, a specialized version of ChatGPT designed specifically for cybersecurity and ethical hacking. With cyberattacks occurring every 39 seconds and causing billions in damages, HackerGPT provides crucial tools and knowledge to protect against these threats. Discover how HackerGPT can guide you through a variety of topics, including performing security assessments, ethical hacking techniques, and scripting for system security. HackerGPT adheres strictly to ethical guidelines, ensuring all guidance aligns with legal and professional standards. Learn how to become a skilled cybersecurity professional, equipped to defend against cyber threats ethically and effectively. Join us for this engaging and informative tutorial, and enhance your cybersecurity skills with HackerGPT.

Here in this Ethical HackerGPT Tutorial, we cover the following topics:

00:00 Introduction to Cyber Security
02:10 Exploring Ethical Hacker GPT
04:33 Asking General Cybersecurity prompts from Ethical HackerGPT
06:05 Asking Ethical Hacking prompts from Ethical HackerGPT
07:33 Asking Scripting and Automation prompts from Ethical HackerGPT
09:39 Asking Specific Security Tools prompts from Ethical HackerGPT
10:47 Asking Incident Response prompts from Ethical HackerGPT
12:40 Asking Advanced Topics prompts from Ethical HackerGPT
15:55 Asking Cybersecurity Policies and Compliance prompts from Ethical HackerGPT

FAQs

1. What is HackerGPT and how does it work?
HackerGPT is a specialized version of ChatGPT designed for cybersecurity and ethical hacking. It provides guidance on a wide range of security topics, including vulnerability assessments, ethical hacking techniques, and scripting for system security. HackerGPT helps users understand and mitigate potential cyber threats while adhering to legal and ethical guidelines.

2. How can HackerGPT help improve my cybersecurity skills?
HackerGPT offers expert advice and practical solutions for various cybersecurity challenges. It covers topics such as security assessments, ethical hacking, scripting for automation, and using security tools like Burp Suite and Nmap. This guidance helps users enhance their knowledge and skills in defending against cyber threats.

3. Is HackerGPT safe and ethical to use?
Yes, HackerGPT is designed to follow strict ethical guidelines. It does not provide assistance for any queries deemed unethical or illegal. Its purpose is to help users learn and practice cybersecurity techniques responsibly, ensuring all guidance aligns with legal and professional standards.

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#HackerGPT #hacker #cybersecurity #EthicalHacking #Hacking #2024 #simplilearn

⏩ Check out the Cybersecurity videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUJfPO

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This cybersecurity expert program readies individuals for CEH, CompTIA Security+, and CISSP certifications, offering a holistic defense strategy against dynamic cyber threats. CEH emphasizes ethical hacking, CompTIA focuses on foundational security concepts, and CISSP covers advanced principles and management.

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MIT 6.100L Introduction to CS and Programming using Python, Fall 2022
Instructor: Ana Bell

View the complete course: https://ocw.mit.edu/courses/6-....100l-introduction-to
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

An introduction to what computation is, python basics, mathematical operations, and python variables and types. We explore python objects and python programs.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
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We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐌𝐏 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐚𝐦 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/pmp-cer....tification-exam-trai 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka tutorial on Project Quality Management will give you an insight into the various process and activities covered during Project development in order to maintain and manage the quality of a project.
00:00:00 - Introduction
00:01:47 - Project Quality Management
00:05:42 - Quality Management Overview
00:12:40 - Quality Management Processes

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
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Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 (Use code: "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/data-sc....ience-certification-
This Edureka "Statistics & Probability For Data Science" video will introduce you to the concepts of statistics using Data Science. You will learn about Descriptive and Inferential statistics in detail with a demo. The following topics are covered in this Statistics for Data Science tutorial:
00:00:00 Introduction
00:01:11 Probability
00:02:13 Probability - Terminologies
00:03:33 Probability - Types of Events
00:04:46 Probability - Distribution
00:05:10 Probability - PDF
00:07:00 Probability - Normal Distribution
00:11:36 Probability - Central Limit Theorem
00:11:40 Probability - Types of Probability
00:12:01 Probability - Marginal Probability
00:12:48 Probability - Joint Probability
00:13:14 Probability - Conditional Probability
00:15:11 Probability - Bayes Theorem
00:16:30 Probability - Applications
00:17:12 Statistics
00:17:40 Statistics - Terminologies
00:19:12 Statistics - Sampling technique
00:21:30 Statistics - Types of Statistics
00:24:26 Statistics - Measures of Center
00:26:02 Statistics - Measures of Spread
00:32:22 Statistics - Hands-on
00:33:26 Statistics - Information Gain
00:38:32 Statistics - Inferential Statistics

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

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00:02:40 How to install C++ on Windows
00:06:42 First program in C++
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00:53:54 Functions in C++
00:57:39 Call by value and Call by reference in C++
01:09:57 Function Example
01:18:38 Recursion
01:37:32 Friend function in C++
01:56:57 Header files in C++
02:00:34 Arrays in C++
02:19:43 Pointers in C++
02:39:51 Pointer in C++
03:00:05 Sorting in C++
03:44:29 References in C++
04:01:14 Memory management in C++
04:19:57 Classes in C++
04:23:31 Constructor and Destructor in C++
04:28:32 Classes Examples
04:52:32 OOP’s in C++
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05:39:53 C++ Polymorphism
06:05:16 C++ Operator overloading
06:22:48 C++ Encapsulation
06:37:56 Exception handling in C++
06:54:11 C++ Vectors
07:22:39 C++ STL
07:47:49 C++ Enums
08:07:15 C++ GUI
08:34:06 Hotel management project in C++
09:19:34 Login and registration system project
09:55:26 Supermarket Billing project
11:17:55 C++ Interview Questions

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐰𝐢𝐭𝐡 𝐓𝐞𝐧𝐬𝐨𝐫𝐟𝐥𝐨𝐰 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: https://www.edureka.co/ai-deep....-learning-with-tenso (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝" video will help you in understanding why we need Transformers and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using Transformers.
00:00:00 Agenda
00:01:28 Introduction to Deep Learning
00:08:24 What is Deep Learning
00:13:02 NLP using RNN
00:15:59 Scaling up NLP task
00:16:49 Transformers Walkthrough
00:19:51 Top Language Models

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GStack is an open-source toolkit built by YC President & CEO Garry Tan that turns Claude Code into an AI engineering team — with skills for office hours, design, code review, QA, and browser testing.

Use it with Claude Code or Codex or Cursor. It's free and open source: https://github.com/garrytan/gstack

In this video, Garry walks through how GStack works, starting with Office Hours, a skill modeled after real YC partner sessions that pressure-tests your idea before you write a line of code. He demos it live, going from idea through adversarial review, design mockups, and automated QA in a single session.

00:00 – AI Just Changed Coding Forever
00:09 – From YC to Building With AI
01:07 – Why AI Coding Feels So Different
02:45 – Turning AI Into a Real Team (GStack)
03:45 – Let’s Build an App Live
05:23 – The Question That Kills Most Ideas
07:13 – This Idea Just Got Way Bigger
08:38 – The “Feels Illegal” AI Hack
10:50 – Upgrading the Idea in Real Time
12:44 – Breaking + Fixing the Plan
14:25 – AI Designs the App
16:59 – The Full System Explained
18:00 – Running Multiple AI Engineers
20:00 – Shipping 10x Faster
21:20 – The Only Thing That Matters Now

Apply to Y Combinator: https://www.ycombinator.com/apply
Work at a startup: https://www.ycombinator.com/jobs

https://www.conductor.build/

Simple and easy explanation of LLM or Large Language Model in less than 5 minutes. In this short video, you will build an intuition of how a large language model works using animation and simple story telling. This is the explanation that even a high school student can understand easily.

Simple Explanation of Neural Network: https://www.youtube.com/watch?v=ER2It2mIagI

Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.

Need help building software or data analytics/AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.

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Direct Preference Optimization (DPO) is a method used for training Large Language Models (LLMs). DPO is a direct way to train the LLM without the need for reinforcement learning, which makes it more effective and more efficient.
Learn about it in this simple video!

This is the third one in a series of 4 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1: Proximal Policy Optimization https://www.youtube.com/watch?v=TjHH_--7l8g
Video 2: Reinforcement Learning with Human Feedback https://www.youtube.com/watch?v=Z_JUqJBpVOk
Video 3 (This one!): Deterministic Policy Optimization

00:00 Introduction
01:08 RLHF vs DPO
07:19 The Bradley-Terry Model
11:25 KL Divergence
16:32 The Loss Function
14:36 Conclusion

Get the Grokking Machine Learning book!
https://manning.com/books/grok....king-machine-learnin
Discount code (40%): serranoyt
(Use the discount code on checkout)

In this video, learn how to build your own Small Language Model (SLM) from scratch using Python, PyTorch, and Hugging Face datasets. We’ll walk through every step — from data collection and preprocessing to tokenization, transformer architecture, and model training.

You’ll discover how a compact 10–15 million parameter model can still generate coherent and meaningful text, using concepts inspired by GPT models. This tutorial simplifies complex AI topics like embedding layers, attention mechanisms, loss computation, and next-token prediction — all explained clearly with working code examples.

🔹 Topics Covered:

What are Small Language Models (SLMs)?

Using the TinyStories dataset from Hugging Face

Tokenization with OpenAI’s tiktoken library

Creating .bin training files for efficient processing

Building a Transformer architecture step-by-step

Implementing GPT-like attention blocks

Training configuration and hyperparameter tuning

Generating text from your trained SLM

🎯 Perfect for AI researchers, ML engineers, and enthusiasts who want to understand how GPT-style models actually work under the hood — without requiring massive GPUs or billion-parameter setups.

#SmallLanguageModel #BuildLLM #GPT2Tutorial #TransformerModel #PyTorch #AIML #DeepLearning #NLP #HuggingFace #TrainYourOwnGPT #SLM #LanguageModel #MachineLearning #ArtificialIntelligence #NanoGPT #AIProjects #PythonCoding #AICoding #OpenSourceAI #generativeai

Simple solution to the #kaggle competition which obtains a final score of over 0.92 which would rank us in the top 1% of submissions! :)

Link to santander customer transaction prediction:
https://www.kaggle.com/c/santa....nder-customer-transa

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Timestamps:
0:00 - Introduction to competition
1:50 - Get data
8:22 - Simple NN baseline
20:50 - First results 0.86 score
21:33 - Understanding the data
23:40 - Modifying our NN
28:10 - Improvement to baseline
29:00 - Feature engineering
44:54 - Modifying our NN v2
50:45 - Final result and submission
56:50 - Ending