Admissions Open for JANUARY Batch
Explore mathematical foundations of neural networks and optimization techniques
Days : Tue & Thu
Duration : 10 Hours
Timings: 8 - 10 PM IST
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1 Months
5 Hours
Tue & Thu
Deep Learning Math
( Neural Network Core )
This module dives into the math that runs deep learning models, like neural networks. You’ll see how
computers learn automatically by adjusting weights and reducing errors. You’ll also learn about the
functions that help models recognize shapes, images, or patterns.
Online Live Instructor-Led Learning
10 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Matrix calculus and gradient computation
Activation and loss functions math
Get familier with
Backpropagation and weight optimization
Convolution for feature extraction
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners progressing to deep learning, with emphasis on neural network math, activation functions, optimization techniques, and backpropagation crucial for building advanced AI model.
Prerequisites
Strong understanding of ML math and calculus.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Strong understanding of ML math and calculus.
Who Should Enroll?
This course is for learners progressing to deep learning, with emphasis on neural network math, activation functions, optimization techniques, and backpropagation crucial for building advanced AI model.
By end of this course
Get Stronger in
- Matrix calculus and gradient computation
- Activation and loss functions math
Get Familiar in
- Backpropagation and weight optimization
- Convolution for feature extraction
Course Contents
What is covered: Calculating derivatives for matrices and vectors.
Application: Training deep neural networks, computing gradients.
Example:
1. Backpropagation: Calculating how much each weight in a neural network should change.
2. Gradient Calculation: Finding the direction to adjust parameters for better predictions.
What is covered: Algorithm for updating weights in neural networks.
Application: Training deep learning models.
Example:
1.Image Classification: Neural network learns to recognize cats vs dogs by adjusting weights using backpropagation.
2.Speech Recognition: Improving accuracy by learning from mistakes
What is covered: Functions that introduce non-linearity (ReLU, sigmoid), and measure error (loss).
Application: Enabling neural networks to learn complex patterns.
Example:
1.ReLU Activation: Helps deep networks learn features like edges in images.
2.Softmax Loss: Used for multi-class classification (e.g., digit recognition).
What is covered: Methods to find best model parameters (SGD,Adam).
Application: Efficient training, faster convergence.
Example:
1. Adam Optimizer: Used in training large models like GPT for faster and stable learning.
2. Learning Rate Scheduling: Adjusting how quickly a model learns over time.
What is covered: Mathematical operation for extracting features from data (images).
Application: Computer vision, image processing.
Example:
1. Edge Detection: Convolutional layers in CNNs find edges and shapes in images.
2. Feature Maps: Identifying objects in photos.
What is covered: Calculating derivatives for matrices and vectors.
Application: Training deep neural networks, computing gradients.
Example:
1. Backpropagation: Calculating how much each weight in a neural network should change.
2. Gradient Calculation: Finding the direction to adjust parameters for better predictions.
What is covered: Algorithm for updating weights in neural networks.
Application: Training deep learning models.
Example:
1.Image Classification: Neural network learns to recognize cats vs dogs by adjusting weights using backpropagation.
2.Speech Recognition: Improving accuracy by learning from mistakes
What is covered: Functions that introduce non-linearity (ReLU, sigmoid), and measure error (loss).
Application: Enabling neural networks to learn complex patterns.
Example:
1.ReLU Activation: Helps deep networks learn features like edges in images.
2.Softmax Loss: Used for multi-class classification (e.g., digit recognition).
What is covered: Methods to find best model parameters (SGD,Adam).
Application: Efficient training, faster convergence.
Example:
1. Adam Optimizer: Used in training large models like GPT for faster and stable learning.
2. Learning Rate Scheduling: Adjusting how quickly a model learns over time.
What is covered: Mathematical operation for extracting features from data (images).
Application: Computer vision, image processing.
Example:
1. Edge Detection: Convolutional layers in CNNs find edges and shapes in images.
2. Feature Maps: Identifying objects in photos.