(in Polish) Introduction to deep learning for natural language processing 3800-KOG-MS2-IDL

The course aims to provide the students with a basic understanding of Neural Networks, so they can build and train models using Python and TensorFlow to solve tasks for Natural Language Processing. Specific topics include:
- Basic mathematics for deep learning (gradient descent, matrices, probability theory)
- Basic Python libraries for deep learning (pandas, NumPy, Matplotlib)
- Introduction to machine learning (linear regression, logistic regression)
- The perceptron (activation function, loss function, backpropagation)
- Metrics to evaluate machine learning algorithms (confusion matrix, F1 score)
- Deep learning Python libraries (TensorFlow and Keras)
- Vector space models (bag of words, n-grams, word embeddings)
- Convolutional neural networks
- Recurrent neural networks
- Other deep learning model architectures (LSTM, GRU, and the Transformer)
- Practical examples for NLP (text classification, sentiment analysis)