This comprehensive TensorFlow and Keras course is designed for learners who want to master deep learning and neural network development. The course starts with the fundamentals of neural networks and the prerequisites required to understand deep learning concepts. Students will learn how to set up TensorFlow and Keras with GPU support, process and prepare datasets, and build artificial neural networks using Keras APIs.
The course covers model training, validation techniques, prediction generation, confusion matrix evaluation, and model saving and loading. Learners will gain practical experience working with real-world machine learning workflows and understand how to improve model performance through proper validation and testing strategies.
In addition, the course introduces Convolutional Neural Networks (CNNs), one of the most important architectures in computer vision. Students will learn image preprocessing techniques, CNN architecture design, training procedures, and image classification predictions using TensorFlow and Keras. By the end of the course, learners will be able to create, train, evaluate, and deploy deep learning models for a variety of machine learning and computer vision applications. This course is ideal for aspiring AI engineers, machine learning practitioners, data scientists, and developers seeking hands-on experience with modern deep learning frameworks.
