A collection of useful references for AI/ML research and development.
- https://github.com/checkcheckzz/system-design-interview#-company-engineering-blogs
- https://developer.nvidia.com/blog
- https://neptune.ai/blog
- https://ruder.io/
- https://huyenchip.com/blog/
- https://openai.com/
- https://www.deeplearning.ai/
- https://research.google/
- https://www.amazon.science/research-areas/machine-learning
- https://www.microsoft.com/en-us/research/research-area/artificial-intelligence/?
- https://research.netflix.com/
- https://machinelearning.apple.com/
- https://ai.facebook.com/
- https://research.atspotify.com/machine-learning/
- Crash Couse - Computer Science
- https://www.youtube.com/c/IBMTechnology
- https://www.youtube.com/user/googlecloudplatform
- https://www.youtube.com/c/amazonwebservices/featured
- The A.I. Hacker
- https://www.youtube.com/c/StanfordMLSysSeminars
- https://www.youtube.com/c/AndrejKarpathy/videos
- Two Minutes Papers
- https://www.youtube.com/c/YannicKilcher
- https://www.youtube.com/c/AICoffeeBreak
- https://www.youtube.com/c/AssemblyAI
- https://www.youtube.com/c/PyTorch
- https://www.youtube.com/c/TensorFlow
- https://www.youtube.com/c/HuggingFace
- https://www.youtube.com/c/WeightsBiases/featured
- https://leetcode.com/
- https://www.educative.io/path/ace-python-coding-interview
- https://www.algoexpert.io/product
- Indexing
- https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html
- https://mml-book.github.io/book/mml-book.pdf
- https://neptune.ai/blog/continuous-integration-continuous-deployment-tools-for-machine-learning
- https://docs.github.com/en/actions
- https://circleci.com/blog/top-5-ci-cd-best-practices/
- https://madewithml.com/courses/mlops/data-stack/
- https://www.snowflake.com/wp-content/uploads/2019/12/11-best-practices-for-data-engineers.pdf
- https://datavaultalliance.com/about/what-is-datavault/
- https://www.stardog.com/knowledge-graph/
- https://cloud.google.com/blog/topics/developers-practitioners/definitive-guide-databases-google-cloud-part-1-data-modeling-basics
- https://neo4j.com/developer/graph-database/
- https://cloud.google.com/blog/products/ai-machine-learning/analyze-graph-data-on-google-cloud-with-neo4j-and-vertex-ai
- https://d2l.ai/index.html
- One machine learning question every day - bnomial
- https://developers.google.com/machine-learning/crash-course/ml-intro
- https://www.algoexpert.io/machine-learning/crash-course
- https://www.educative.io/courses/machine-learning-for-software-engineers
- https://www.learnpytorch.io/
- https://pytorch.org/tutorials/
- https://www.tensorflow.org/resources/learn-ml
- https://www.tensorflow.org/tutorials
- https://scikit-learn.org/stable/tutorial/index.html
- https://developers.google.com/machine-learning/guides/rules-of-ml
- Hidden Technical Debt in Machine Learning Systems
- https://www.youtube.com/c/StanfordMLSysSeminars
- https://github.com/donnemartin/system-design-primer
- https://github.com/checkcheckzz/system-design-interview
- Educative.io - Grokking The Machine Learning Interview
- https://neptune.ai/blog/improving-machine-learning-deep-learning-models
- Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (YouTube video)
- https://www.tensorflow.org/ and https://keras.io/
- https://pytorch.org/ and https://www.pytorchlightning.ai/
- https://scikit-learn.org/stable/
- https://huggingface.co/
- https://opencv.org/
- https://www.ray.io/ray-tune
- https://lambdalabs.com/
- https://wandb.ai/site
- https://mlflow.org/
- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
- https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
- https://neptune.ai/blog/category/mlops
- https://madewithml.com/courses/mlops/design/
- https://www.oreilly.com/live-events/deploying-nlp-models-in-production-using-mlops/0636920064921/0636920070043/
- https://neptune.ai/blog/category/natural-language-processing
- https://github.com/GoogleCloudPlatform/mlops-with-vertex-ai
- https://www.tensorflow.org/tfx
- https://pytorch.org/torchx/latest/
- https://www.kubeflow.org/
- https://github.com/jacopotagliabue/you-dont-need-a-bigger-boat/tree/main/remote_flow
- https://cloud.google.com/vertex-ai
- AWS SageMaker
- AWS SageMaker MLOps templates
- https://huggingface.co/models
- https://www.tensorflow.org/hub
- https://pytorch.org/hub/
- https://github.com/CompVis/stable-diffusion
- A Survey of Deep Active Learning
- https://www.manning.com/books/human-in-the-loop-machine-learning
- Human-And-Model-in-the-Loop Enabled Training (HAMLET)
- https://baal.readthedocs.io/en/latest/
- https://github.com/rmunro/pytorch_active_learning
- https://www.geeksforgeeks.org/ml-active-learning/
- https://github.com/google/active-learning
- https://burrsettles.com/pub/settles.activelearning.pdf
- https://keras.io/examples/nlp/active_learning_review_classification/
- https://github.com/modAL-python/modAL
- https://github.com/facebookresearch/Adversarial-Continual-Learning
- AWS SageMaker A2I
- https://cloud.google.com/vertex-ai/docs/samples/aiplatform-create-data-labeling-job-active-learning-sample
- https://c3.ai/wp-content/uploads/2020/06/Best-Practices-in-Developing-an-Enterprise-AI-Roadmap.pdf
- https://c3.ai/wp-content/uploads/2020/06/Best-Practices-in-Creating-CoE.pdf
- https://c3.ai/wp-content/uploads/2020/06/Best-Practices-in-Enterprise-AI-App-Dev.pdf
- https://oecd.ai/en/ai-principles
- https://ai.google/principles/
- https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework
- https://www.microsoft.com/en-us/ai/responsible-ai
- https://ai.google/responsibilities/responsible-ai-practices/
- https://github.com/microsoft/responsible-ai-toolbox
- https://www.tensorflow.org/responsible_ai
- https://modelcards.withgoogle.com/about
- https://mlco2.github.io/impact/