Deep Learning

Understands the fundamentals of Deep Learning. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Knows deep learning fundamentals: backpropagation, loss functions, optimizers (SGD, Adam). Understands neural network architecture and trains simple models on PyTorch under mentor guidance.

Independently designs and trains deep learning models for production tasks. Works with CNN, RNN/LSTM, Transformer architectures. Applies regularization (dropout, batch norm, weight decay), optimizes hyperparameters through systematic search.

Independently trains and fine-tunes models with PyTorch: configures learning rate schedules, regularization, and data augmentation. Understands gradient flow in transformer architectures.

Designs complex deep learning architectures: multi-task learning, attention mechanisms, generative models (VAE, GAN). Optimizes training through mixed precision, distributed training, gradient accumulation. Applies knowledge distillation for model deployment.

Designs custom training loops for LLM: mixed precision, gradient accumulation, distributed training. Diagnoses training issues: gradient vanishing/exploding, loss spikes, training instability.

Defines deep learning strategy for the data science team. Establishes training infrastructure standards and model architecture guidelines. Evaluates state-of-the-art approaches and makes decisions on their production adoption.

Defines deep learning best practices for the LLM team. Establishes model training standards, conducts training configuration reviews, introduces training run monitoring systems.

Shapes organizational deep learning strategy. Defines investments in GPU infrastructure, evaluates custom vs pre-trained models. Publishes research, shapes the organization's scientific and technical leadership in DL.

Shapes organizational deep learning practices strategy. Defines approaches to pre-training and fine-tuning large models, mentors leads on advanced training techniques.