AI/ML BootCamp - Batch 01 - Session 05

COMSATS University Islamabad - Abbottabad, Pakistan

Welcome to Session 05 of the AI/ML BootCamp - Batch 01! In this session, participants will dive into Gradient Descent, a fundamental optimization algorithm used to minimize the cost function in machine learning models. Through interactive lectures and hands-on exercises, attendees will learn how to implement and apply Gradient Descent to optimize their models effectively.

Nov 25, 2023, 4:00 – 5:00 PM

36 RSVP'd


Key Themes

Cloud Study JamExplore MLGeminiGoogle CloudKaggleML Study JamMachine LearningOpen SourceSolution ChallengeTensorFlow / Keras

About this event

Session 05 of the AI/ML BootCamp - Batch 01 is a crucial part of your machine learning education, focusing on Gradient Descent, a cornerstone algorithm for training machine learning models. This session will provide participants with an in-depth understanding of Gradient Descent, its variations, and practical applications in optimizing machine learning algorithms.

Introduction to Gradient Descent

The session will start with an overview of Gradient Descent, explaining its importance in machine learning for optimizing cost functions. Participants will learn about the concept of gradients, the cost function, and how Gradient Descent iteratively adjusts model parameters to find the minimum of the cost function.

Mathematical Foundations

Participants will delve into the mathematical foundations of Gradient Descent. This includes understanding the derivative of the cost function, the learning rate, and the update rule. The session will also cover the importance of choosing an appropriate learning rate and the effects of different learning rates on the convergence of the algorithm.

Types of Gradient Descent

The session will explore various types of Gradient Descent algorithms, including:

Batch Gradient Descent: Participants will learn how this version uses the entire dataset to compute the gradient and update the parameters in each iteration.

Stochastic Gradient Descent (SGD): This version updates the parameters for each training example, providing faster but noisier convergence.

Mini-Batch Gradient Descent: A hybrid approach that uses a subset of the data to balance the benefits of both batch and stochastic methods.

Implementing Gradient Descent

Through hands-on exercises, participants will implement Gradient Descent from scratch using Python. They will learn how to initialize parameters, compute gradients, and iteratively update parameters to minimize the cost function. Practical examples will demonstrate the application of Gradient Descent in linear regression and logistic regression.

Advanced Topics and Best Practices

The session will also cover advanced topics such as momentum, learning rate schedules, and adaptive learning rate methods like AdaGrad, RMSprop, and Adam. These techniques enhance the performance of Gradient Descent and help overcome challenges like slow convergence and local minima.

Interactive Learning and Practical Exercises

Participants will benefit from interactive lectures that break down complex concepts into understandable segments, coupled with practical coding exercises that reinforce learning. By working on real-world datasets, they will gain hands-on experience in applying Gradient Descent to optimize their models.

Session Highlights:

Understand the role of Gradient Descent in machine learning.

Learn the mathematical foundations of Gradient Descent.

Explore different types of Gradient Descent algorithms.

Implement Gradient Descent from scratch using Python.

Discover advanced techniques to improve Gradient Descent performance.

Join us for Session 05 of the AI/ML BootCamp - Batch 01, and master the art of optimizing machine learning models with Gradient Descent. Whether you're new to optimization algorithms or looking to refine your skills, this session will equip you with the essential tools and knowledge to advance in the field of machine learning.

Don't miss this opportunity to deepen your understanding of Gradient Descent and enhance your model optimization capabilities with the AI/ML BootCamp - Batch 01!


  • Rida Zainab


    AI/ML Ninja


  • Muhammad Raees Azam

    GDSC COMSATS Abbottabad


  • Rizwan Shah

    GDSC COMSATS Abbottabad



  • Muhammad Raees Azam

    GDSC Lead

  • Hashir Ahmad Khan

    Former General Secretary

  • Maha Babar

    Comsats University

    Co- Lead

  • Nayab Zahra

    COMSATS University Islamabad

    Industrial & PR GURU


    Comsat University Islamabad Abbottabad Campus

    Information Technology Guru

  • Ibrahim Mir

    Comsats university abbottabad

    General Secretary

  • Areeb Ajab

    C.U.I, Abbottabad Campus

    Android Ninja

  • Sara Iftikhar

    COMSATS University Islamabad, Abbottabad Camous.

    Graphics Ninja

  • Wania Khan

    COMSATS University Islamabad, Abbottabad Campus.

    Membership Ninja

  • Muneer Hasan

    Flutter Ninja

  • Rida Zainab

    AI/ML Ninja

  • Muhammad Awais Khan

    Comsat University Islamabad Abbottabad Campus

    Web Ninja

  • Maria Adil


    Documentation Ninja

  • Varisha Sajjad

    Comsats University Abbottabad

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  • Muhammad Hasnain

    Media Ninja

  • Muhammad Danyal

    Comsats Abbotabad

    Membership Ninja ( M )

  • Jawaid Aziz


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  • Malik Imran

    Comsats university abbottabad

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    COMSATS University Abbottabad Campus

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