The advanced block is more complex topics that make you already a strong professional AI developer
Writing neural networks with the Tensorflow library. Part 1
Writing neural networks with the Tensorflow library. Part 2
Writing neural networks with the PyTorch library. Part 1
Writing neural networks with the PyTorch library. Part 2
AutoML: images, tables, texts, time series
Computer vision: object detection, segmentation, OCR, OpenCV
Speech recognition and generation
Natural language processing: Bert, T5, NER, transformers, NLTK, pymorphy2, RNN, LSTM
The basics of working with chatGPT. Promt engineering. Embedding representation of texts. LangChain algorithm. Creation of knowledge bases. Creating a dialog with chatGPT. Connecting multiple chatGPT models
Working with GigaChat. Running and using SoTA (state of the art) local (contour) models
Classical machine learning, clustering and recommender systems
Publish to server, FastApi
Working with databases: SQL, MS SQL, PostgreSQL, Oracle
Fundamentals of distributed data processing: Spark, Hadoop and HDFS
Tools and frameworks for working with big data: PySpark, Hive and ClickHouse
Orchestration and workflow management: AirFlow, MLFlow and MLOps
Tools for project management and teamwork: Confluence, Jira, Git and Gitlab
Streaming and containerization: Kafka, CI/CD and Kubernetes