Machine Learning & Deep Learning
Domain Overview & Technical Focus
Comprehensive foundation in training, fine-tuning, and evaluating neural network architectures across vision, sequence modeling, and decision-making agents with deep reinforcement learning.
Core Frameworks, Tools & Methodologies
Specific technologies and their practical application in research & software engineering
PyTorch
Primary deep learning framework used for custom neural architectures, reinforcement learning environments, and academic research implementations.
TensorFlow & Keras
Applied for foundational deep learning coursework, convolutional networks, and baseline reproducibility.
Scikit-learn
Standard statistical machine learning algorithms, classification, regression, clustering, dimensionality reduction, and model evaluation pipelines.
Hugging Face Transformers
Utilizing pretrained foundation models, tokenizers, parameter-efficient fine-tuning (PEFT/LoRA), and inference pipelines for natural language processing.
Deep Reinforcement Learning
Policy gradient methods, PPO, Actor-Critic architectures, reward modeling, and continuous action-space control.
Generative Models (GANs & LSTMs)
Adversarial network architectures, generative time-series modeling (LSTM-GAN), and latent representation modeling.
Out-of-Distribution & Anomaly Detection
Confidence estimation, Mahalanobis distance scoring, and spectral anomaly detection techniques in mission-critical settings.
Representation Learning
Self-supervised feature extraction, contrastive embeddings, and latent representations for downstream classification.