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Neural Networks and Deep Learning
2 students
Neural Networks and Deep Learning
Master the Foundations of Neural Networks and Modern Deep Learning
Created byPralhad Teggi
Last updated 7/2025
English
What you'll learn
- Learners will grasp foundational concepts of ANN, including perceptron, multi-layer architectures, activation functions, and training mechanisms.
- Learners will be able to implement and compare various gradient descent optimization methods to train neural networks effectively.
- Learners will understand bias-variance trade-offs and apply methods like L1/L2 regularization, dropout, and batch normalization to improve model generalization
- By the end of the course, learners will be able to build, train, and evaluate NN on real datasets, including MNIST and a water quality classification problem.
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Course content
7 sections • 37 lectures • 2h 21m total length
Requirements
- Basic Python Programming - Learners should be familiar with basic Python syntax, variables, loops, and functions.
- Fundamentals of Mathematics - A working knowledge of high school-level linear algebra (vectors, matrices), probability, and calculus (basic derivatives) is recommended.
- Basic Machine Learning Concepts - Some understanding of core ML ideas like classification, training/testing data, and supervised learning will be helpful (but not mandatory).
- Motivation to Learn and Experiment - A strong willingness to understand how deep learning works from the ground up and apply it to real-world problems using hands-on coding.
Description
Are you ready to dive deep into the powerful world of Neural Networks and Deep Learning? Whether you're a student, data science enthusiast, or an early-career AI professional, this course will help you build a solid foundation in modern neural architectures — from perceptrons to multi-layered networks — and master the mechanics behind how they learn.
What You’ll Learn:
Understand what neural networks are and how they’re inspired by the human brain.
Build simple ANNs from scratch for basic logic operations (OR, AND, NAND).
Dive into Perceptrons and Multi-Layer Perceptrons (MLP), learning how they process data through forward propagation.
Master key concepts like loss functions, cost functions, and gradient descent, including the difference between partial derivatives and gradients.
Implement and compare optimization techniques like batch, stochastic, mini-batch, momentum, and RMSProp gradient descent.
Learn how to prevent overfitting using techniques such as L1/L2 regularization, dropout, and batch normalization.
Apply these concepts in practical use cases, including water quality contamination detection and MNIST digit recognition.
Key Highlights:
Visual and intuitive explanations for gradient descent and error surfaces
Practical walkthroughs for regularization methods using real-world scenarios
Hands-on use cases demonstrating the power of neural networks in real-world problems
Emphasis on interpreting and improving model performance
Who this course is for:
- Beginner to Intermediate Learners who want a practical and intuitive introduction to neural networks and deep learning, without heavy math.
- Aspiring Data Scientists and ML Engineers looking to build a strong foundation in artificial neural networks and understand how models like MLPs and gradient descent work.
- Python Programmers and Developers who want to transition into AI/ML and apply neural networks to real-world problems.
- Students and Researchers interested in understanding and experimenting with deep learning models for academic or project-based work.
- Professionals in Engineering, Finance, or Environmental Science who want to leverage AI to solve domain-specific problems, like classification and forecasting.