Deep Neural Networks: Python Fundamentals
Master deep neural networks from scratch using Python. Learn gradient descent, implement DNNs with NumPy, and work on real datasets.
Master deep neural networks from scratch using Python. Learn gradient descent, implement DNNs with NumPy, and work on real datasets.
This comprehensive course takes beginners through the fundamentals of deep neural networks using Python. Starting with basic concepts, students learn to implement DNNs from scratch using NumPy. The curriculum covers essential topics like gradient descent, neural network architecture, and optimization techniques. Through hands-on projects and real-world datasets like IRIS, learners gain practical experience in building and training neural networks. The course combines theoretical understanding with practical implementation, making it ideal for data science enthusiasts.
Instructors:
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What you'll learn
Understand the basics of training a DNN using the Gradient Descent algorithm
Apply knowledge to implement a complete DNN using NumPy
Analyze and create a complete structure for DNN from scratch using Python
Evaluate and work on a project using deep learning for the IRIS dataset
Master data preprocessing and general machine learning concepts
Skills you'll gain
This course includes:
384 Minutes PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 5 modules in this course
This comprehensive course provides a solid foundation in deep neural networks using Python. Starting with basic concepts and progressing to advanced implementations, students learn to build neural networks from scratch. The curriculum covers essential topics including gradient descent algorithms, perceptrons, error functions, and optimization techniques. Through practical coding exercises and real-world datasets, learners develop hands-on experience in implementing deep learning solutions. The course emphasizes both theoretical understanding and practical application.
Introduction
Module 1 · 19 Minutes to complete
Basics of Deep Learning
Module 2 · 2 Hours to complete
Deep Learning
Module 3 · 2 Hours to complete
Optimizations
Module 4 · 38 Minutes to complete
Final Project
Module 5 · 2 Hours to complete
Fee Structure
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Instructor
Enhancing IT Education Through Expert-Led Learning
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