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In this module, you will learn the foundational concepts and architectures behind Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). You’ll start with the basics of neural networks, exploring essential components like layers and activation functions, and understanding the training process through backpropagation and gradient descent. You’ll then dive into CNNs for image recognition, RNNs for sequence data processing, and GANs for generating new data. The module also covers regularization techniques like dropout and batch normalization to improve model performance. Finally, you’ll get hands-on experience with popular deep learning tools and frameworks, including TensorFlow, Keras, and PyTorch, allowing you to build, train, and evaluate neural networks. This comprehensive module will equip you with both theoretical and practical knowledge to design and implement deep learning models across various applications.

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