Mastering artificial neural networks (ANNs) requires understanding their structure, training processes, and practical applications. Begin by studying key concepts like forward propagation, backpropagation, and activation functions.
Learn to build ANNs using frameworks like TensorFlow or PyTorch. Start with simple models, then progress to advanced architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Practice using popular datasets like MNIST, CIFAR-10, or IMDB for hands-on experience.
Explore optimization techniques, such as learning rate scheduling and regularization, to improve model performance. Regularly implement projects and stay updated with research papers to deepen your expertise.