This comprehensive guide provides an introduction to various topics in the field of Artificial Intelligence and Machine Learning. It includes resources for further learning for each topic.
Machine learning algorithms enable computers to learn from and make decisions or predictions based on data.
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- Supervised Learning with scikit-learn (course by DataCamp)
- Supervised Learning Video (video by Google Developers)
- The Elements of Statistical Learning (free book)
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- Unsupervised Learning with Python (tutorial by DataCamp)
- Unsupervised Learning: Foundations of Neural Networks, Deep Learning, and Machine Learning (video by 3Blue1Brown)
- Clustering in Machine Learning (Google Developers)
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- Reinforcement Learning: An Introduction (free book)
- Deep Reinforcement Learning (free course by Coursera)
- DeepMind's series on Reinforcement Learning (video series by DeepMind)
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- Semi-Supervised Learning with Scikit-Learn (tutorial by scikit-learn)
- Semi-Supervised Learning Literature Survey (free literature survey)
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- A Comprehensive Hands-on Guide to Transfer Learning (article by Towards Data Science)
- Transfer Learning - Machine Learning's Next Frontier (article by Sebastian Ruder)
- Deep Learning Specialization (free course by Coursera)
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- Ensemble Learning in Python (tutorial by DataCamp)
- Understanding Ensemble Methods (article by Towards Data Science)
- Ensemble Learning to Improve Machine Learning Results (article by Statsbot Blog)
Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from vast amounts of data.
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- Neural Networks and Deep Learning (free online book by Michael Nielsen)
- Intro to Deep Learning with PyTorch (free course by Udacity)
- But what is a Neural Network? (video by 3Blue1Brown)
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Convolutional Neural Networks (CNNs)
- Convolutional Neural Networks for Visual Recognition (free course by Stanford)
- Convolutional Neural Networks (CNNs / ConvNets) (article by Stanford CS231n)
- A Comprehensive Guide to Convolutional Neural Networks (article by Towards Data Science)
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Recurrent Neural Networks (RNNs)
- The Unreasonable Effectiveness of Recurrent Neural Networks (article by Andrej Karpathy)
- Understanding LSTM Networks (article by Christopher Olah)
- Recurrent Neural Networks in Tensorflow I (tutorial by R2RT)
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Generative Adversarial Networks (GANs)
- NIPS 2016 Tutorial: Generative Adversarial Networks (free paper/tutorial)
- GANs from Scratch (free book)
- Generative Adversarial Networks Explained (video by Siraj Raval)
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- The Illustrated Transformer (blog post by Jay Alammar)
- Attention Is All You Need (original Transformer paper)
- Transformer model for language understanding (tutorial by TensorFlow)
NLP is a branch of AI that gives the machines the ability to read, understand, and derive meaning from human languages.
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- Text Classification with Python and Scikit-Learn (tutorial by Stack Abuse)
- Deep Learning for Text Classification with Keras (tutorial by Towards Data Science)
- Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK (tutorial by Towards Data Science)
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Named Entity Recognition (NER)
- Named Entity Recognition with NLTK and SpaCy (tutorial by Towards Data Science)
- Train a Named Entity Recognition (NER) Model with MITIE (tutorial by Ravi Kiran)
- Stanford Named Entity Recognizer (NER) (software by Stanford NLP Group)
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- Twitter Sentiment Analysis using Python (tutorial by GeeksforGeeks)
- Sentiment Analysis: Concept, Analysis and Applications (article by Towards Data Science)
- Python for NLP: Sentiment Analysis with Scikit-Learn (tutorial by Stack Abuse)
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- Sequence to Sequence Learning with Neural Networks (original Seq2Seq paper)
- Neural Machine Translation by Jointly Learning to Align and Translate (original Attention Mechanism paper)
- Neural Machine Translation with Attention (tutorial by TensorFlow)
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- How to build a Question-Answering System (tutorial by Towards Data Science)
- Question Answering with a Fine-Tuned BERT (tutorial by Chris McCormick)
- Stanford Question Answering Dataset (SQuAD) (dataset for training QA models)
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- The Unreasonable Effectiveness of Recurrent Neural Networks (tutorial by Andrej Karpathy)
- Generating Text with Recurrent Neural Networks (paper by Ilya Sutskever et al.)
- How to Generate Text: Using Different Decoding Methods for Language Generation with Transformers (tutorial by Hugging Face)
Computer Vision is a field of artificial intelligence that trains computers to interpret and understand the visual world.
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- CS231n: Convolutional Neural Networks for Visual Recognition
- Deep Learning for Computer Vision (free course by Udacity)
- Image Recognition with Neural Networks (article by Towards Data Science)
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- Object Detection with TensorFlow (tutorial by TensorFlow)
- Object Detection using Deep Learning Approaches: An End to End Theoretical Perspective (academic review paper)
- How to Easily Detect Objects with Deep Learning on Raspberry Pi (tutorial by DLology)
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- Semantic Segmentation with Deep Learning (tutorial by MathWorks)
- Image Segmentation Using Deep Learning: A Tutorial (academic tutorial)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (original U-Net paper)
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- Image Captioning with Visual Attention (tutorial by TensorFlow)
- Automatic Image Captioning using Deep Learning (CNN and LSTM) in PyTorch (tutorial by Analytics Vidhya)
- Show and Tell: A Neural Image Caption Generator (original Image Captioning paper)
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- Face Recognition with Python, in Under 25 Lines of Code (tutorial by Real Python)
- OpenFace: Free and open source face recognition with deep neural networks (Open-source project)
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification (original DeepFace paper)
Robotics is an interdisciplinary field that integrates computer science and engineering.
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- MIT 6.141: Robotics: Science and Systems I (free course by MIT)
- The 10 most important breakthroughs in Artificial Intelligence (article by Emerj)
- Introduction to Autonomous Robots (free course by Coursera)
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Robotic Process Automation (RPA)
- Introduction to Robotic Process Automation (RPA) (article by UiPath)
- Robotic Process Automation Tutorial (video by Edureka)
- RPA Beginner's Guide (article by Automation Shift)
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- Human-Robot Interaction: an overview (academic survey paper)
- Stanford University's Interactive Autonomy and Collaborative Technologies Lab (lab website with many relevant resources)
- MIT's Human and Automation Lab (lab website with many relevant resources)
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Reinforcement Learning for Robotics
- Deep Reinforcement Learning for Robotics (video by Pieter Abbeel)
- A Brief Survey of Deep Reinforcement Learning (academic survey paper)
- Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion (academic paper on efficient reinforcement learning)
Expert systems are computer systems that emulate the decision-making ability of a human expert.
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- Introduction to Knowledge Based Systems (free course by NPTEL)
- Building a Knowledge-Based System (research paper)
- Artificial Intelligence - Knowledge Based Systems (tutorial by TutorialsPoint)
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- Rule Based Systems (ebook chapter)
- Building Rule-Based Systems (tutorial by DeepLearningBook.org)
- What are Rule-Based Systems? (video by Diffbot)
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- Inference Engines (article by ISSCO)
- The Role of Inference Engines in Decision Support Systems (book chapter available for free preview)
- Inference Engines and Decision Making (video by KnowledgeBase Ninja)
It's a field of AI that focuses on representing knowledge in a form that a computer system can utilize to solve complex tasks such as diagnosis and planning.
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- Introduction to Ontologies (video by Ontology Summit)
- Ontology Development 101 (guide by Stanford's Protégé project)
- Ontology Engineering (free preview of book)
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- A First Order Logic Tutorial (video by Brandon Foltz)
- Introduction to Logic (article by Stanford Encyclopedia of Philosophy)
- First-Order Logic - Coursera (free course by Coursera)
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- Introduction to Semantic Web (video by IBM Developer)
- Linked Data: Evolving the Web into a Global Data Space (free online book)
- Semantic Web and Linked Data (free course by Coursera)
Speech Recognition involves identifying and converting spoken language into text by computers.
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Automatic Speech Recognition (ASR)
- An Introduction to Automatic Speech Recognition (video by NVIDIA)
- Introduction to Automatic Speech Recognition (conference paper)
- Automatic Speech Recognition: A Deep Learning Approach (free preview of book)
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- Speech to Text Transcription with Google Cloud Speech-to-Text API (video by Google Cloud Tech)
- Tutorial: Converting Speech to Text with DeepSpeech and Python (tutorial by PyImageSearch)
- Introduction to Speech Recognition (free course by Coursera)
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Speech Synthesis (Text-to-Speech)
- Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions (research paper by Google)
- Text-to-Speech Tutorial (tutorial by Google Cloud)
- Text to Speech Deep Learning Architectures (video by NVIDIA)
Virtual agents are AI systems that can interact with people in a humanlike manner.
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- Building Conversational AI with Machine Learning (article by Google AI Blog)
- Introduction to Conversational Software and Agents (video by edX)
- Conversational AI: Building clever chatbots (free course by Coursera)
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- Deep Learning for Dialogue Systems (research paper)
- Designing Dialogue Systems (research paper)
- Dialogflow: Build Engaging Voice and Text-Based Conversational Interfaces (video by Google Cloud Tech)
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- How to Develop a Chatbot from Scratch (tutorial by ChatbotsLife)
- How to Build a Chatbot without Coding (video by IBM Cloud)
- Building a Simple Chatbot from Scratch in Python (tutorial by Towards Data Science)
Autonomous vehicles are capable of sensing their environment and operating without human involvement.
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- Introduction to Self-Driving Cars (free course by Coursera)
- The Road to Full Autonomy: Self-Driving Cars (video by Waymo)
- Self-Driving Cars: A Case Study in Making New Markets (article by Harvard Business School)
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Advanced Driver Assistance Systems (ADAS)
- What is ADAS? How Advanced Driver Assist Systems work (video by ADAS.ie)
- Overview of Advanced Driver Assistance Systems (ADAS) (research paper)
- Advanced Driver Assistance Systems: Challenges and Opportunities Ahead (article by McKinsey)
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Computer Vision for Autonomous Vehicles
- Computer Vision for Autonomous Vehicles (article by Towards Data Science)
- Applications of Computer Vision in Autonomous Vehicles (video by Learn Engineering)
- Computer Vision in Autonomous Vehicles: Object Detection (article by Nanonets)
Recommender systems are algorithms aimed at suggesting relevant items to users.
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- Introduction to Collaborative Filtering (article by Analytics Vidhya)
- Collaborative Filtering Recommender Systems (video by Luis Serrano)
- Collaborative Filtering for Netflix (research paper by Netflix)
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- Content-based Recommender Systems (article by Analytics Vidhya)
- Content-Based Recommendation System using Word Embeddings (tutorial by Towards Data Science)
- Content-based Recommender System: Machine Learning Approach (video by Krish Naik)
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- Hybrid Recommender Systems: Survey and Experiments (research paper)
- Hybrid Recommender Systems: A Systematic Literature Review (article by IntechOpen)
- Designing a Hybrid Recommendation System (video by Packt Video)
AI techniques have been applied in game playing scenarios like chess, Go, and video games to develop algorithms that can play these games effectively.
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- Deep Blue’s victory over chess champion Garry Kasparov (article by IBM)
- AlphaZero AI Teaches Itself Chess (video by ChessNetwork)
- Building a Chess Engine from Scratch (article by freeCodeCamp)
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- AlphaGo - The Story So Far (article by DeepMind)
- AlphaGo's historic match against Go champion Lee Sedol (video by DeepMind)
- Mastering the game of Go with deep neural networks and tree search (research paper by Nature)
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- Creating a Basic Game AI with Pathfinding (tutorial by Gamedev Tutsplus)
- Implementing AI in Games (video by Extra Credits)
- AI and Games (free course by Coursera)
Knowledge graphs are a way to structure and interpret complex, interlinked data.
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- Graph-based Features for Supervised Link Prediction (research paper by PLoS ONE)
- Graph-Based Semi-Supervised Learning (video by Stanford University School of Engineering)
- Introduction to Graph Theory (tutorial by Geeks for Geeks)
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- A Quick Guide to Semantic Web and Linked Data Concepts (article by Towards Data Science)
- Introduction to Semantic Web (video by IBM Developer)
- Building Semantic Graphs for Content Recommendation (article by DZone)
Cognitive computing involves self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works.
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- The Rise of Emotion AI (article by Affectiva)
- Emotion AI Explained (video by Wall Street Journal)
- Emotion AI: Understanding its impact and ethical considerations (article by IBM Research)
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- Context-Aware Computing, AI, and Machine Learning (research paper by Taylor & Francis)
- Context Aware Computing for The Internet of Things: A Survey (research paper by IEEE Xplore)
- Context-Aware Systems and Applications (book series by Springer)
Swarm Intelligence is the collective behavior of decentralized, self-organized systems, natural or artificial.
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- Ant Colony Optimization - Tutorial (article by ScienceDirect)
- Ant Colony Optimization: A Brief Introduction (video by Artificial Intelligence Hub)
- Ant Colony Optimization: Algorithms and Applications (research paper by IEEE Xplore)
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- Particle Swarm Optimization (PSO) - A Tutorial (article by ScienceDirect)
- Particle Swarm Optimization Explained (video by The Coding Train)
- Particle Swarm Optimization: Theory, Techniques and Applications (book by Amazon)