Machine Learning

Overview Popular Machine Learning Algorithms Machine Learning Use Cases Popular ML Models

Popular ML Models

Machine learning models and architectures have been extensively researched, and many of these models have been open-sourced by researchers, making them available for public use. They can typically be accessed via platforms such as TensorFlow Hub, PyTorch Hub, or the Hugging Face Model Hub. Here's a categorization based on their application:

  1. Image Classification:
    • VGG (VGG16, VGG19): Developed by the Visual Graphics Group for image recognition.
    • ResNet: Includes ResNet50, ResNet101, etc., designed to counteract the problem of deep networks by introducing skip connections.
    • InceptionV3: Known for its computational efficiency.
    • MobileNet: Efficient for mobile vision applications.
  2. Object Detection:
    • YOLO (You Only Look Once): Real-time object detection.
    • SSD (Single Shot MultiBox Detector): Offers a balance between speed and accuracy.
    • Faster R-CNN: Uses Region Proposal Networks (RPN) for object detection.
  3. Natural Language Processing:
    • BERT (Bidirectional Encoder Representations from Transformers): For tasks like question answering and sentiment analysis.
    • GPT-2 and GPT-3 (Generative Pre-trained Transformer): Language models known for generating coherent paragraphs of text.
    • Transformer: The underlying architecture for models like BERT and GPT.
    • RoBERTa: A modified BERT model with different pre-training data and strategy.
  4. Generative Models:
    • GAN (Generative Adversarial Network): For generating new data samples.
    • DCGAN (Deep Convolutional GAN): A GAN variant using convolutional networks.
    • CycleGAN: For tasks like image-to-image translation without paired data.
  5. Audio and Speech:
    • WaveNet: Generates raw audio waveforms, used for tasks like speech synthesis.
    • DeepSpeech: By Mozilla, for speech-to-text conversion.
  6. Transfer Learning:
    • ULMFiT (Universal Language Model Fine-tuning): For transferring pre-trained language models to custom tasks.
    • T5 (Text-to-Text Transfer Transformer): Views every NLP problem as a text-to-text problem.
  7. Tabular Data:
    • CatBoost: Specialized in handling categorical variables.
    • LightGBM: Gradient boosting framework that uses tree-based algorithms.
    • XGBoost: An optimized gradient boosting library.
  8. Reinforcement Learning:
    • DQN (Deep Q-Network): Combines Q-learning with deep neural networks.
    • PPO (Proximal Policy Optimization): Makes minor updates to the policy that's being used to select actions, enhancing stability.
  9. Anomaly Detection:
    • Isolation Forest: Uses tree structures for anomaly scoring.
    • AutoEncoder: Neural networks useful for anomaly detection in high-dimensional datasets.
  10. Time Series:

·         Prophet: Developed by Facebook for forecasting time series data.

·         LSTM (Long Short-Term Memory): Recurrent Neural Network architecture suitable for time series predictions.

  1. Text Generation:

·         XLNet: Uses a permutation-based training approach for generating coherent text.

·         Transformer-XL (Transformer with extra-long context): Handles longer context than traditional transformers.

  1. Face Recognition:

·         FaceNet: Embeds faces into a compact space using triplets.

·         ArcFace: Enhances the discriminative power of face embeddings.

  1. Image Segmentation:

·         U-Net: A convolutional neural network for biomedical image segmentation.

·         Mask R-CNN: Extends Faster R-CNN to generate segmentation masks.

  1. Style Transfer:

·         Neural Style Transfer (NST): Uses deep learning to superimpose art styles onto images.

·         CycleGAN: Translates styles between unpaired image datasets.

  1. Video Analysis:

·         3D-CNN (Three-Dimensional Convolutional Neural Networks): Extends the 2D CNN to handle video data.

·         I3D (Inflated 3D ConvNet): Adapts 2D ConvNets to 3D for video classification.

  1. Drug Discovery and Healthcare:

·         DeepChem: Deep learning for drug discovery.

·         MoleculeNet: Benchmark for molecular machine learning.

  1. Translation and Language Models:

·         OpenNMT: Neural machine translation framework.

·         Marian NMT: Fast neural machine translation in C++.

  1. Graph-based Models:

·         GCN (Graph Convolutional Network): Applies convolutions directly on graph-structured data.

·         GAT (Graph Attention Network): Uses attention mechanisms for graph-based data.

  1. Recommender Systems:

·         Neural Collaborative Filtering (NCF): Combines traditional collaborative filtering with neural networks.

·         Matrix Factorization: A technique for user-item recommendation.

  1. Few-shot and Zero-shot Learning:

·         Siamese Networks: Uses twin networks to measure similarity.

·         Matching Networks: Designed for one-shot learning tasks.

·         ZSL (Zero-Shot Learning): Models that classify objects seen during training into new unseen classes.