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Machine Learning (ML)
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- Snippet from Wikipedia: Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance.
ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics.
Statistics and mathematical optimization (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning.
From a theoretical viewpoint, probably approximately correct learning provides a framework for describing machine learning.
External Sites
Machine Learning: Google Gemini, ChatGPT, ML Fundamentals, ML Inventor: Arthur Samuel of IBM 1959 coined term Machine Learning. Synonym Self-Teaching computers from 1950s. Experimental AI “Learning Machine” called Cybertron in early 1960s by Raytheon Company; ChatGPT, NLP, GAN, ML, DL - Deep learning - Python Deep learning, MLOps, Python machine learning (sci-kit, OpenCV, TensorFlow, PyTorch, Keras, NumPy, NLTK, SciPy, sci-kit learn, Seaborn, Matplotlib), Cloud ML (AWS ML, Azure ML, Google ML-GCP ML-Google Cloud ML, IBM ML, Apple ML), C Plus Plus Machine Learning | C++ Machine Learning, C Sharp Machine Learning | Machine Learning, Golang Machine Learning, Java Machine Learning, JavaScript Machine Learning, Julia Machine Learning, Kotlin Machine Learning, R Machine Learning, Ruby Machine Learning, Rust Machine Learning, Scala Machine Learning, Swift Machine Learning, ML History, ML Bibliography, Manning AI-ML-DL-NLP-GAN Series, ML Glossary, ML Topics, ML Courses, ML Libraries, ML Frameworks, ML GitHub, ML Awesome List. (navbar_ml - See also navbar_dl, navbar_nlp, navbar_chatgpt, navbar_ai, navbar_tensorflow, borg_usage_disclaimer)
Terms related to: AI-ML-DL-NLP-GenAI-LLM-GPT-RAG-MLOps-Chatbots-ChatGPT-Gemini-Copilot-HuggingFace-GPU-Prompt Engineering-Data Science-DataOps-Data Engineering-Big Data-Analytics-Databases-SQL-NoSQL
AI, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Neural Network, Generative AI (GenAI), Natural Language Processing (NLP), Large Language Model (LLM), Transformer Models, GPT (Generative Pre-trained Transformer), ChatGPT, Chatbots, Prompt Engineering, HuggingFace, GPU (Graphics Processing Unit), RAG (Retrieval-Augmented Generation), MLOps (Machine Learning Operations), Data Science, DataOps (Data Operations), Data Engineering, Big Data, Analytics, Databases, SQL (Structured Query Language), NoSQL, Gemini (Google AI Model), Copilot (AI Pair Programmer), Foundation Models, LLM Fine-Tuning, LLM Inference, LLM Training, Parameter-Efficient Tuning, Instruction Tuning, Few-Shot Learning, Zero-Shot Learning, One-Shot Learning, Meta-Learning, Reinforcement Learning from Human Feedback (RLHF), Self-Supervised Learning, Contrastive Learning, Masked Language Modeling, Causal Language Modeling, Attention Mechanism, Self-Attention, Multi-Head Attention, Positional Embeddings, Word Embeddings, Tokenization, Byte Pair Encoding (BPE), SentencePiece Tokenization, Subword Tokenization, Prompt Templates, Prompt Context Window, Context Length, Scaling Laws, Parameter Scaling, Model Architecture, Model Distillation, Model Pruning, Model Quantization, Model Compression, Low-Rank Adaptation (LoRA), Sparse Models, Mixture of Experts, Neural Architecture Search (NAS), AutoML, Gradient Descent Optimization, Stochastic Gradient Descent (SGD), Adam Optimizer, AdamW Optimizer, RMSProp Optimizer, Adagrad Optimizer, Adadelta Optimizer, Nesterov Momentum, Learning Rate Schedules, Warmup Steps, Cosine Decay, Hyperparameter Tuning, Bayesian Optimization, Grid Search, Random Search, Population Based Training, Early Stopping, Regularization, Dropout, Weight Decay, Label Smoothing, Batch Normalization, Layer Normalization, Instance Normalization, Group Normalization, Residual Connections, Skip Connections, Encoder-Decoder Architecture, Encoder Stack, Decoder Stack, Cross-Attention, Feed-Forward Layers, Position-Wise Feed-Forward Network, Pre-LN vs Post-LN, Sequence-to-Sequence Models, Causal Decoder-Only Models, Masked Autoencoder, Domain Adaptation, Task-Specific Heads, Classification Head, Regression Head, Token Classification Head, Sequence Classification Head, Multiple-Choice Head, Span Prediction Head, Causal Head, Next Sentence Prediction, MLM (Masked Language Modeling), NSP (Next Sentence Prediction), C4 Dataset, WebText Dataset, Common Crawl Corpus, Wikipedia Corpus, BooksCorpus, Pile Dataset, LAION Dataset, Curated Corpora, Fine-Tuning Datasets, Instruction Data, Alignment Data, Human Feedback Data, Preference Ranking, Reward Modeling, RLHF Policy Optimization, Batch Inference, Online Inference, Vector Databases, FAISS Integration, Chroma Integration, Weaviate Integration, Pinecone Integration, Milvus Integration, Data Embeddings, Semantic Search, Embedding Models, Text-to-Vector Encoding, Vector Similarity Search, Approximate Nearest Neighbor (ANN), HNSW Index, IVF Index, ScaNN Index, Memory Footprint Optimization, HuggingFace Transformers, HuggingFace Hub, HuggingFace Datasets, HuggingFace Model Cards, HuggingFace Spaces, HuggingFace Inference Endpoints, HuggingFace Accelerate, HuggingFace PEFT (Parameter Efficient Fine-Tuning), HuggingFace Safetensors Format, HuggingFace Tokenizers, HuggingFace Pipeline, HuggingFace Trainer, HuggingFace Auto Classes (AutoModel, AutoTokenizer), HuggingFace Model Conversion, HuggingFace Community Models, HuggingFace Diffusers, Stable Diffusion, HuggingFace Model Hub Search, HuggingFace Secrets Management, OpenAI GPT models, OpenAI API, OpenAI Chat Completions, OpenAI Text Completions, OpenAI Embeddings API, OpenAI Rate Limits, OpenAI Fine-Tuning (GPT-3.5, GPT-4), OpenAI System Messages, OpenAI Assistant Messages, OpenAI User Messages, OpenAI Function Calls, OpenAI ChatML Format, OpenAI Temperature Parameter, OpenAI Top_p Parameter, OpenAI Frequency Penalty, OpenAI Presence Penalty, OpenAI Max Tokens Parameter, OpenAI Logit Bias, OpenAI Stop Sequences, Azure OpenAI Integration, Anthropic Claude Integration, Anthropic Claude Context Window, Anthropic Claude Constitutional AI, Cohere Integration LLM provider, Llama2 (Meta's LLM), Llama2 Chat Model, Vicuna Model (LLM)), Alpaca Model, StableLM, MPT (MosaicML Pretrained Transformer), Falcon LLM, Baichuan LLM, Code Llama, WizardCoder Model, WizardLM Model, Phoenix LLM, Samantha LLM, LoRA Adapters, PEFT for LLM, BitFit Parameters Tuning, QLoRA (Quantized LoRA), GLoRA, GGML Quantization, GPTQ Quantization, SmoothQuant, Int4 Quantization, Int8 Quantization, FP16 Mixed Precision, BF16 Precision, MLOps Tools, MLOps CI/CD, MLOps CD4ML, MLOps Feature Store, MLOps Model Registry, MLOps Model Serving, MLOps Model Monitoring, MLOps Model Drift Detection, MLOps Data Drift Detection, MLOps Model Explainability Integration, MLOps MLFlow Integration, MLOps Kubeflow Integration, MLOps MLRun, MLOps Seldon Core for serving, MLOps BentoML for serving, MLOps MLflow Tracking, MLOps MLflow Model Registry, MLOps DVC (Data Version Control), MLOps Delta Lake, RAG (Retrieval-Augmented Generation), RAG Document Store, RAG Vector Store Backend, RAG Memory Augmentation, RAG On-the-fly Retrieval, RAG Re-ranking Step, RAG HyDE Technique - It's known as hypothetical document embeddings - advanced but known in RAG, RAG chain-of-thought, chain-of-thought related to LLM reasoning, Chain-of-Thought Reasoning, Self-Consistency Decoding, Tree-of-thoughts, ReAct (Reason+Act) Prompting Strategy, Prompt Engineering Techniques, Prompt Templates (LLM), Prompt Variables Replacement, Prompt Few-Shot Examples, Prompt Zero-Shot Mode, Prompt Retrieval Injection, Prompt System Message, Prompt Assistant Message, Prompt Role Specification, Prompt Content Filtering, Prompt Moderation Tools, AI-Generated Code Completion, Copilot (GitHub) Integration, CoPilot CLI, Copilot Labs, Gemini (Google Model) Early access, LLM from Google, LaMDA (Language Model for Dialog Applications), PaLM (Pathways Language Model), PaLM2 (PaLM 2 Model), Flan PaLM Models, Google Vertex AI Integration, AWS Sagemaker Integration, Azure Machine Learning Integration, Databricks MLFlow Integration, HuggingFace Hub LFS for large models, LFS big files management, OPT (Open Pretrained Transformer) Meta Model, Bloom LLM, Ernie Bot (Baidu LLM), Zhipu-Chat - Another LLM from China, Salesforce CodeT5 - It's a code model, Finetune with LoRA on GPT-4, Anthropic Claude 2
Artificial Intelligence (AI): The Borg, SkyNet, Google Gemini, ChatGPT, AI Fundamentals, AI Inventor: Arthur Samuel of IBM 1959 coined term Machine Learning. Synonym Self-Teaching Computers from 1950s. Experimental AI “Learning Machine” called Cybertron in early 1960s by Raytheon Company; ChatGPT, Generative AI, NLP, GAN, AI winter, The Singularity, AI FUD, Quantum FUD (Fake Quantum Computers), AI Propaganda, Quantum Propaganda, Cloud AI (AWS AI, Azure AI, Google AI-GCP AI-Google Cloud AI, IBM AI, Apple AI), Deep Learning (DL), Machine learning (ML), AI History, AI Bibliography, Manning AI-ML-DL-NLP-GAN Series, AI Glossary, AI Topics, AI Courses, AI Libraries, AI frameworks, AI GitHub, AI Awesome List. (navbar_ai - See also navbar_dl, navbar_ml, navbar_nlp, navbar_chatbot, navbar_chatgpt, navbar_llm, navbar_openai, borg_usage_disclaimer, navbar_bigtech, navbar_cia)
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