Deep Learning Basics

Discover the exciting world of deep learning and neural networks in this hands-on course, designed to build a strong foundation in artificial intelligence. Through interactive lessons and real-world projects, you'll gain the skills to design, build, and train deep learning models using Keras, a user-friendly and powerful library for AI development.

What You'll Learn:

• Core Concepts: Understand the fundamentals of neural networks, deep learning models, and how they work.
• Supervised and Unsupervised Models: Explore supervised models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), as well as unsupervised models like autoencoders.
• Hands-On Development: Use the Keras library to create, train, and evaluate deep learning models for tasks such as regression and classification.
• Advanced Topics: Gain a deeper understanding of critical topics like backpropagation, gradient descent, and the role of activation functions in model performance.
• Practical Applications: Solve real-world challenges by building convolutional networks, experimenting with model depth and width, and optimizing model performance.

Course Requirements:

To get the most out of this course, you’ll need:

• A good understanding of Python programming to implement and experiment with deep learning models.
• A working knowledge of Algebra 2, including concepts like functions, logarithms, and basic matrix operations.

This course is ideal for learners looking to enhance their AI skills, prepare for advanced studies, or gain practical experience in one of the most in-demand fields today. Get started and take your knowledge of machine learning to the next level!