Akademik Dersler - İçerik

Weeks	Topics
Week - 1	The course begins with an introduction to the concepts of artificial intelligence, machine learning, and deep learning, explaining the differences between them, their historical development, and their place in the context of big data.
Week - 2	The fundamentals of artificial neural networks, the artificial neuron model, activation functions, and simple feedforward networks are covered.
Week - 3	Covers how networks are trained using forward propagation, error functions, and the backpropagation algorithm.
Week - 4	Moves on to optimization methods; algorithms such as SGD, Momentum, RMSProp, and Adam are examined with examples.
Week - 5	Addresses the issue of overfitting; regularization techniques, dropout, and data augmentation methods are covered.
Week - 6	Deep learning libraries used in the Python environment (TensorFlow, PyTorch) are introduced, and a small neural network application is implemented.
Week - 7	Covers multi-layer deep neural networks (DNN) in detail and applies them to simple classification problems.
Week - 8	The logic of convolutional neural networks (CNN), layer structures, and their applications in image processing.
Week - 9	Recurrent neural networks (RNN); their use in sequential data such as time series and text is explained alongside LSTM and GRU models.
Week - 10	Natural language processing applications; embedding methods, Word2Vec, and basic text classification studies are conducted.
Week - 11	Introduces encoder–decoder architectures and seq2seq models, and provides an introduction to the attention mechanism.
Week - 12	Focuses on modern approaches; the Transformer architecture is examined in detail, and the fundamental principles of models such as BERT and GPT are discussed.
Week - 13	Covers different application areas of deep learning (image, text, and audio processing) and its integration with big data ecosystems.
Week - 14	Features presentations of term projects by students, a summary of topics covered in class, and an evaluation of future trends in deep learning.