Machine Learning (ML)
- Languages: Python, C/C++, SQL, JavaScript/TypeScript, HTML, CSS, bash
- Deep Learning libs: Pytorch, TensorFlow, JAX/Flax
- ML libs: Pandas, NumPy, Scikit-Learn, SciPy
- Gradient Boosting: XGBoost, LightGBM, CatBoost
- Scaling/Parallelization: Dask
- AutoML (Hyperparameter Optimization): Optuna, Ray Tune, AutoKeras, auto-sklearn, Auto-PyTorch, AutoGluon, Amazon SageMaker Autopilot, Google AutoML, Azure AutoML, H20 AutoML
- Wrapper libs: PyCaret
- Visualization: Matplotlib, Plotly, Bokeh
- Feature selection & explainability: LIME, SHAP, Boruta
- Containerization: Docker, Kubernetes, Helm
- Cloud Computing: AWS, GCP, Azure
- Infrastructure as Code (IaC): Ansible, Terraform
- Python interactive web applications: Streamlit, Gradio, Dash
- Python (server-side) web frameworks: Django, Flask, FastAPI, BentoML
- JavaScript (client-side) frameworks: React (Meta) / Next.js, Angular (Google), Vue, Svelte
- Tools: Git, VSCode, Visual Studio, Jupyter, Colab, GitHub Copilot, Cursor, Codium, W&B
- Supervised Learning:
- Classical: Linear/Logistic Regression (Ridge [L2]/Lasso [L1]/Elastic Net [L1+L2]), SVM, Naive Bayes, K-NN, Decision Trees, Random Forest, GBM
- Unsupervised Learning:
- Clustering: K-Means, DBSCAN, GMM, Hierarchical Clustering
- Dimensionality reduction: PCA, UMAP, tSNE, LDA, Autoencoder, ICA
- Advanced ML techniques:
- Generative AI + NLP (Natural Language Processing)
- Techniques: Attention mechanism, Transformer architecture, CLIP, Diffusion Models, PEFT, (Q)LoRA, RAG [1], Ragas, RAPTOR, SSM (Mamba, Jamba), RLHF, RLAIF, DPO [1], LoLCAT [1]
- Libs: OpenAI, Hugging Face Transformers, LangChain, LangGraph, LlamaIndex, GraphRAG, DSPy [1],
LlamaHub, litellm, Guidance, NLTK, spaCy, Genism - LLM (Large Language Models): GPT-4 (OpenAI), Llama 3 (Meta), Claude 3 (Opus, Sonnet, Haiku) (Anthropic), Gemini (Ultra, Pro, Nano) (Google)
- Cloud Inference: OpenRouter, Groq, Fireworks, Together.AI, Replicate, Anyscale, Cerebras, Lepton AI, Deep Infra, Novita.AI, TGI
- Local Inference: LM Studio, Olama, vllm, Jan.ai
- ChatBots: ChatGPT, Copilot, Gemini, Claude, Meta AI (Llama), Grok, Character.ai, Poe, Pi, Perplexity, You
- Conversational AI: Azure Bot Service / Copilot Studio (Microsoft), Lex (Amazon), Dialogflow (Google), IBM Watson Assistant, Rasa
- Text to Image: Dall-E3 (OpenAI), Midjourney, Flux (Black Forest Labs), Stable Diffusion [1] / Stable Assistant (Stability.ai), Imagine (Meta), Leonardo, Adobe Firefly, Segmind, Ideogram, ComfyUI
- Text to Video: Sora (OpenAI), Dream Machine (Luma AI), Gen 3 (Runway), Kling (Kuaishou), Pika, Hailuo (MiniMax), Stable Video Diffusion, Haiper, Emu (Meta), Lumiere (Google), Mochi 1 (Genmo)
- Text to Music: Suno, Udio
- Text to Speech: ElevenLabs
- Agents: overview, Autogen , OpenAI Assistants API, AutoGPT, BabyAGI, GPT-Engineer, MetaGPT, OpenInterpreter [1], OpenAgents, Sweep, Microsoft UFO
- RL (Reinforcement Learning)
- Libs: Acme (DeepMind), RLlib (UC Berkeley), Stable Baselines 3 (DLR), Keras-RL2, OpenAI Baselines, garage
- GANs (Generative Adversarial Networks)
- GNNs (Graph Neural Networks)
- SSL (Self Supervised Learning)
- Generative AI + NLP (Natural Language Processing)
Quantum Computing &
Quantum Machine Learning (QML)
- Quantum computing libs:
- Algorithms:
- QFT (Quantum Fourier Transform), 1994
- QPE (Quantum Phase Estimation), 1995
- Shor’s factorization algorithm, 1995
- QEC (Quantum Error Correction), 1996, 2024
- Grover search, 1996
- QAA (Quantum Amplitude Amplification), 1997, 2000
- QRAM (Quantum Random Access Memory), 2007
- HHL (Harrow, Hassidim, Loyd), Quantum Linear Algebra, 2008
- QPCA (Quantum Principal Component Analysis), 2013
- QSVM (Quantum Support Vector Machine), 2013
- VQE (Variational Quantum Eigensolver), 2013
- QAOA (Quantum Approximative Optimization Algorithm), 2014
- QBM (Quantum Boltzmann Machine), 2016
- QEKF (Quantum Extended Kalman Filter), 2016
- QRS (Quantum Recommendation Systems), 2016
- QSVD (Quantum Singular Value Decomposition), 2016
- QGAN (Quantum Generative Adversarial Network), 2018
- VQC (Variational Quantum Classifier), 2018
- QHNN (Quantum Hopfield Neural Network), 2018
- QCNN (Quantum Convolutional Neural Network), 2018
- Analytical gradients on quantum computer (parameter shift rule), 2018
- Q-FFNN (Quantum Feed Forward Neural Network), 2018
- Q-Means (clustering similar to K-means), 2018
- QHL (Quantum Hebbian Learning), 2019
- QEM (Quantum Expectation Maximization), 2019
- VQLS (Variational Quantum Linear Solver), 2019
- QNLP (Quantum Natural Language Processing)
- QRAO (Quantum Random Access Optimization), 2021
- QSANN (Quantum Self Attention Neural Network), 2022
- QRL (Quantum Reinforcement Learning), 2018, 2021
- QUBO (Quadratic Unconstrained Binary Optimization)
- BQM (Binary Quadratic Model), CQM (Constrained Quadratic Model), DQM (Discrete Quadratic Model) solver (D-Wave)
Global Navigation Satellite Systems (GNSS)
- GPS, GLONASS, Galileo, BeiDou, QZSS
- Kalman filter processing, UD-filter
- Ambiguity resolution
- LAMBDA (Least Squares Ambiguity Decorrelation Adjustment)
- BIE (Best Integer Equivariant), iFlex (Integer Floating/Fixing)
- RTK (Real-Time Kinematic) positioning (differential phase processing with ambiguity resolution with difference to a physical reference station, or to a virtual reference station (VRS) in network RTK)
- RTX (Real-Time eXtended) positioning (Trimble’s PPP [Precise Point Positioning] solution, differential phase processing with ambiguity resolution with difference to a global virtual reference station)
- RTX Integrity
- INS (Inertial Navigation System) fusion
- Video fusion
