This course introduces the fundamentals of deep learning using Python, TensorFlow, and Keras, with a strong focus on practical implementation. It is designed for beginners who want to understand how neural networks work and how to apply them to real-world problems.
The course begins with a general introduction to deep learning concepts and how TensorFlow and Keras are used to build and train neural networks. You will learn how to load and work with your own datasets, which is an essential skill for applying machine learning in real scenarios.
A major part of the course focuses on convolutional neural networks (CNNs), which are widely used for image processing tasks. You will also learn how to analyze and optimize models using TensorBoard, a powerful visualization tool for monitoring training performance and improving results.
The course then explores how to use trained models for predictions and practical applications. It also introduces recurrent neural networks (RNNs), which are used for sequential data such as time series and text.
A real-world project is included where you apply RNNs to cryptocurrency prediction, helping you understand how deep learning models are used in real applications.
By the end of this course, you will have a solid understanding of deep learning basics and hands-on experience building and evaluating models using TensorFlow and Keras.
