Introduction to Neural Networks

Master the fundamentals of neural networks and deep learning. Build, train, and apply simple neural networks using cutting-edge frameworks like TensorFlow and PyTorch.

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What will you learn?

This course takes you through the exciting world of neural networks, starting with their structure and functionality. You will uncover how neural networks learn through activation functions, backpropagation, and gradient descent. From there, you will explore real-world applications like image recognition and natural language processing. The course offers hands-on exercises in building and training simple neural networks using popular frameworks such as TensorFlow and PyTorch.

Key takeaways

Understand neural network basics
Learn activation functions and backpropagation
Build and train neural networks
Explore real-world deep learning applications

Program

Module

What are Neural Networks: Learn their structure, components, and purpose.
Activation Functions: Explore how networks learn and make decisions.
Training Neural Networks: Master the principles of backpropagation and gradient descent.
Applications of Neural Networks: Delve into image recognition and NLP.
Building a Neural Network: Hands-on experience with simple models.

Who is it for?

This course is designed for aspiring data scientists, machine learning enthusiasts, and developers looking to understand neural networks and deep learning fundamentals.

Requirements

Basic knowledge of the topic is beneficial.