Explore the basics of deep learning, the significance of Keras, and how it simplifies model development using TensorFlow. Learn about neural networks, their applications, and the advantages of using Keras.

Gain insights into different Keras model architectures. Understand when to use the Sequential model and how to leverage the Functional API for building complex deep learning models.

Learn about the different types of layers, including dense, convolutional, and recurrent layers. Explore activation functions like ReLU, sigmoid, and softmax to optimize model performance.

Understand how to preprocess data efficiently for deep learning tasks. Explore data augmentation techniques and learn how to handle structured and unstructured data.

Discover the importance of compiling models correctly. Learn about different loss functions, optimizers such as Adam and SGD, and techniques to improve model convergence.

Train deep learning models using Keras, evaluate their performance using metrics, and fine-tune hyperparameters for better results.

Explore advanced features of Keras, including callbacks for early stopping, learning rate scheduling, and model checkpointing. Learn how to create custom layers and loss functions.

Learn how to save and load Keras models, deploy them using TensorFlow Serving, and integrate trained models into applications.