Базовая часть

  • Math for AI

    calculus:
    function
    function limit
    logarithms
    derivative
    partial derivative
    gradient
    chain rule

    probability theory:
    discrete random variables
    continuous random variables
    probability function
    probability density function
    mean
    variance and standard deviation
    independent events
    joint probability
    conditional probability

    algebra:
    vectors
    matrices
    operation with vectors and matrices
    determinant

  • Python for AI

    basics recap:
    git flow
    project structure
    code styling
    documentation standards
    decorators

    data manipulation:
    efficient math operations (NumPy)
    accelerating numerical computations (Numba)
    exploratory data analysis (pandas)
    performance speed optimization (Cython)

    ML algorithms:
    basic image/video processing (OpenCV)
    high-level machine learning (scikit-learn)
    deep dive into neural networks (PyTorch)
    forefront NLP (Transformers)

    data visualization:
    visualization fundamentals (Matplotlib)
    high-level plotting (Seaborn)
    interactive visualizations (Plotly)
    web-oriented tools (Bokeh)
    geospatial data plotting (Vega-Altair)

  • AI: from Basics to Transformers. Part 1

    supervised learning
    regression
    regularization
    classification
    metrics
    logistic regression

Продвинутая часть

  • Decision making in AI

    task formulation
    task analysis
    data collection
    data annotation
    exploratory data analysis
    proof of concept development
    analysis of PoC results
    improvement strategy
    getting to the final result
    presenting results

  • MLOps

    data:
    data management (MongoDB, ClickHouse, PostgreSQL)
    data processing manipulations (EDA with Plotly Dash, Streamlit)

    model:
    model engineering: hyperparameters tuning (Optuna, Ray Tune)
    prepare model for production (tracing, quantization, ONNX, TorchServe)

    ML pipeline:
    training/validation/evaluation logging
    experiments tracking (Neptune.ai/MLflow)

    serving:
    deployment tools (Docker, FastAPI)
    memory management and optimization
    testing in MLOps scenarios (PyTest, Profilers)

  • AI: from Basics to Transformers. Part 2

    intro to DL:
    intro to deep learning
    multilayer perceptron
    backpropagation
    deep learning mindset

    CNNs:
    convolutional neural nets
    building convolutional architectures
    residual networks
    CNN training best practices

    transformers:
    attention mechanism in transformer
    multi-head attention
    properties of MHA
    transformer encoder
    transformer decoder
    model interpretation
    transformer training best practices
    vision transformer
    improving ViT
    introduction to language models
    large language models