This TensorFlow course is designed to help beginners learn deep learning and neural network development using one of the most popular machine learning frameworks in the industry. The course starts with installation and environment setup before gradually introducing core TensorFlow concepts.
You will begin by learning about tensors, the fundamental data structures used in TensorFlow, and how they power machine learning computations. The course then demonstrates how to build, train, evaluate, and use your first neural network for prediction tasks.
As your understanding grows, you will work on practical projects such as linear regression and image classification. The course introduces Convolutional Neural Networks (CNNs), which are widely used in computer vision applications including image recognition and object detection.
Additional topics include saving and loading trained models, working with TensorFlow's Functional API, and building multi-output deep learning systems. You will also complete a real-world image classification project and learn transfer learning techniques to improve model performance using pre-trained networks.
The final sections cover Recurrent Neural Networks (RNNs), LSTM and GRU architectures for sequential data processing, along with Natural Language Processing (NLP) applications such as text classification.
By the end of the course, you will have a strong foundation in TensorFlow and practical experience building modern deep learning applications.
