Курсы базовой части

  • Math for AI


    calculus:
    function and function limit
    logarithms
    derivative and partial derivative
    gradient and chain rule
    convex function
    definite and indefinite integrals
    Taylor series

    probability theory:
    discrete and continuous random variables
    probability function / density function
    independent events
    conditional probability
    Bayes’ rule
    expectation, variance
    joint probability
    covariance
    splines
    PCA (Principal component analysis)
    entropy, cross-entropy, relative entropy

    algebra:
    vectors
    matrices
    operation with vectors and matrices
    determinant
    eigenvalues and eigenvector
    spectral decomposition
    SVD (Singular Value Decomposition)

  • 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)

    neural networks (using PyTorch):
    tensor operations
    autograd and computation graphs
    neural network design (torch.nn, layers, forward propagation)
    training and optimization (loss functions, optimizers like SGD and Adam, training loops)
    data handling (Datasets and DataLoader classes)

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

  • AI: from Basics to Transformers. Part 1

    basics:
    supervised learning
    regression
    regularization
    classification
    metrics
    logistic regression

Курсы продвинутой части

  • MLOps


    data:
    data management (ClickHouse)
    data processing manipulations (Plotly Dash, Streamlit)

    model:
    model engineering: hyperparameters tuning (Optuna)
    prepare model for production (TorchScript, ONNX)

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

    serving:
    deployment tools (Docker, FastAPI)

  • Decision making in AI


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

  • 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

    CV:
    image retrieval (NEW)
    image segmentation (NEW)

    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
    time series forecasting with transformer (NEW)

  • AI: from Transformers to LLM. Part 3


    introduction to language models
    large language models
    RoPE (NEW)
    GQA (NEW)
    SwiGLU (NEW)