Sitemap - 2020 - TheSequence
🤝 The AI Consolidation Movement will Continue in 2021
🎲 Edge#50: Facebook's HiPlot and Polygames for Advanced Deep Learning Experimentation
🕑 Edge#49: An Intro to Time-Series Forecasting
💯 The AI Platform Startup Ecosystem is Getting Crowded
☝️ Edge#48: When More Data and Bigger Models can Hurt Performance
🥓 Edge#47: What are Energy-Based Models?
Survey: What technologies will give you an edge in 2021?
📣 Edge#46: Pro-ML is the Architecture Powering Machine Learning at LinkedIn
➡️ Edge#45: Understanding Encoder-Decoder Architectures and Sequence-to-Sequence Learning
🙋🏻♂️Learning from ML practitioners
🎮 Edge#44: DeepMind’s Agent57 Which Outperformed Humans in 57 Atari Games
❄️ Edge#43: Bidirectional Long-Short Term Memory Networks
🔛🔝Salesforce Einstein Brings AutoML Models to a Massive Scale
☕️ Edge#42: What’s New in AI: LinkedIn's Dagli
🍁 Edge#41: Long-Short Term Memory Networks
ℹ️🅿️🌀 The First AI Startups IPOs
📏 Edge#40: On the Measure of Intelligence
🤯 Edge#39: Memory in Deep Learning Architectures
☕️ 🕸 Deep Learning for Java and .NET Developers
📊 Edge#37: What is Model Drift?
⚡️ Keeping Up with AI Research and Technology
⛓ Edge#36: Blockchains, Smart Contracts and Incentives in Decentralized ML
🥦 Edge#35: What is Decentralized AI?
Decentralized AI: Myth or Reality?
🔏 Edge#34: Homomorphic Encryption
💰 Edge#33: The millionaire’s problem and sMPC
🚀 Synthetic Data in ML Models is Becoming Real
🤼 Edge#32: Adversarial Attacks
🔊 Edge#31: Differential Privacy
🗝🚪The Friction Between Privacy and Learning
🔐 Edge#30: Privacy-preserving machine learning
☁️ A Heroku for Machine Learning
🐞 Edge#28: Debugging Machine Learning Models
⚫️ Edge#27: Contrastive learning and a list of Uber’s open-sourced ML contributions
Building Machine Learning with Machine Learning: Myth or Reality?
📗 Edge#24: Text Summarization, Google’s PEGASUS; and Stanford’s CoreNLP in Java
🔂 Edge#20: What is MLOps, top MLOps technologies and Google’s TensorFlow Extended explained~
🔷 End-to-End vs. Best-Of-Breed Machine Learning Platforms~
🔲 Edge#17: Bayesian Neural Networks, how to assess the fairness of a dataset, and Pyro by Uber~
🔶 The Difficult Economics of AI Companies
🔳 Edge#16: Probabilistic Programming, ideas behind MIT’s Gen, and the three most popular PPLs~
👥 Edge#15: Machine Teaching; Uber's Generative Teaching Networks; and Snorkel-Flow~
➿ Does Machine Learning Requires Interoperability?
✨ Edge#14: The magic of semi-supervised learning~
⚖️ Edge#13: Interpretability vs Accuracy
🔥 Quantum Machine Learning is Becoming Real
🌀Edge#12: The challenges of Model Serving~
🍩 Edge#11: The Universe of Meta-Learning~
🚀 The Emerging Market of Data Labeling
🏆 Edge#10: Feature Selection and Feature Extraction
Edge#9: Come across Parallel Training
TheSequence Scope: Can Machine Learning Write Better Machine Learning?
TheSequence Edge#8: GANs – two networks that learn by competing against each other
TheSequence Edge#7: The Generative Models
TheSequence Scope: The Challenge of Data-Efficient Machine Learning
The Sequence Edge#6: Diving Deep into Mobile Deep Learning
The Sequence Edge#5: A practical look into Federated Learning
TheSequence Scope: The Mismatch Between Machine Learning Research and Implementation
Edge#4: Beauty of Neural Architecture Search, and Uber's Ludwig that needs no code
Edge#3: Attention that Transformed Machine Learning
TheSequence Scope: Announcing The Sequence of AI Knowledge
Edge#2: AutoML, AutoML-Zero and the spell of TransmogrifAI
Edge #1: Hyperparameters, The Lottery Ticket Hypothesis, and Weight&Biases platform
TheSequence Scope: Architectures for Building AI at Scale
TheSequence Scope: Systematic AI Education
TheSequence Scope: Bridging the Gap Between Language and Vision in AI Systems
TheSequence Scope: OpenAI Launches its First Product
TheSequence Scope: Is Reinforcement Learning Ready for Prime Time
TheSequence Scope: The Transformer Race
TheSequence Scope: Microsoft's AI Week
TheSequence Scope: Reimagining Enterprise Search with Machine Learning
TheSequence Scope: Self-Supervised vs. Supervised vs. Reinforcement Learning
TheSequence Scope: Faster, Smaller Machine Learning
TheSequence Scope: PyTorch 1.5 is Here
TheSequence Scope: Visualizing Neural Networks
TheSequence Scope: The Biggest Roadblock for the Mainstream Adoption of Machine Learning