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Essential Maths for Deep Learning for beginners
25 students

Essential Maths for Deep Learning for beginners

Deep Learning explained with Easy Numerical, Quizzes, Assignments !
Created byDr AI Academy
Last updated 7/2025
Hindi

What you'll learn

  • Learn Linear Algebra basics: vectors, matrices, operations, transformations—building blocks for deep learning algorithms.
  • Master Calculus essentials: derivatives, gradients—key for optimization techniques like gradient descent in deep learning.
  • Grasp Probability & Statistics: vital for understanding data distributions, uncertainty, and probabilistic models in deep learning.
  • Apply Math to Deep Learning: Translate learned concepts into practical applications—designing, training neural networks for real-world tasks.

Course content

9 sections • 9 lectures • 2h 18m total length

  • ANN With Maths10:34

    "Unraveling Artificial Neural Networks: A Dive into Mathematics and Numerical Examples"


    Join us for an illuminating video lecture as we unravel the intricate workings of Artificial Neural Networks (ANN) through a mathematical lens. Delve into the fundamental principles behind ANN architecture, activation functions, and optimization algorithms, all explained with clarity and supported by numerical examples.


    Through intuitive explanations and step-by-step demonstrations, this lecture demystifies the mathematical foundations of ANN. Learn how to compute forward and backward passes, calculate gradients, and update weights using gradient descent—all essential components for understanding how neural networks learn from data.


    With practical numerical examples, you'll witness firsthand how mathematical concepts come to life in ANN. From basic arithmetic operations to calculus derivatives, we'll guide you through the mathematical machinery that powers neural networks, empowering you to grasp complex concepts with ease.


    Whether you're a novice eager to understand the math behind deep learning or a seasoned practitioner seeking to deepen your understanding, this lecture equips you with the knowledge and confidence to navigate the intricate world of Artificial Neural Networks. Join us on this enlightening journey into the heart of deep learning.

  • ANN With Maths Quiz
  • ANN Assignment

Requirements

  • Familiarity with fundamental mathematical concepts such as arithmetic operations, equations, and basic algebraic manipulations. No prior programming experience required.

Description

Description:

Embark on a transformative journey into the heart of deep learning with our comprehensive course on Essential Maths. Designed for beginners and seasoned practitioners alike, this course provides a solid mathematical foundation essential for understanding and mastering various deep learning algorithms.


Explore key mathematical concepts such as linear algebra, calculus, probability, and statistics, tailored specifically for deep learning applications. Gain proficiency in matrix operations, derivatives, gradients, and probability distributions, vital for building and training neural networks.


Discover how mathematical principles underpin popular deep learning algorithms including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). Dive deep into the mathematics behind each algorithm, unraveling their architectures, operations, and optimization techniques.


Through a combination of theoretical explanations and hands-on numerical examples, you'll develop the skills and intuition necessary to tackle real-world deep learning challenges. Whether you're aiming to break into the field of AI or enhance your existing knowledge, this course equips you with the essential mathematical toolkit to excel in the dynamic world of deep learning.

Topic List:

1. ANN With Maths

2. CNN with Maths

3.RNN with Maths

4.LSTM with Maths

5.GRU with Maths

6. GAN with Maths


Who this course is for:

  • This course is designed for anyone interested in delving into deep learning but lacks a strong mathematical background. Whether you're a beginner or seeking to reinforce your math skills for deep learning, this course caters to all levels of learners.

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