Non-LLM neural networks & ML foundations for intermediate learners
81 resources · page 3 of 3
Karpathy: Unreasonable Effectiveness of RNNs
by Andrej
Andrej Karpathy's 2015 essay The Unreasonable Effectiveness of Recurrent Neural Networks, with char-RNN examples.
LightGBM documentation
LightGBM documentation: gradient boosting framework with tree-based learning, tuning guides and distributed training.
Recurrent Neural Networks and LSTM (Jürgen Schmidhuber)
Jürgen Schmidhuber's IDSIA page on recurrent neural networks and LSTM, with history and references.
NYU Deep Learning (LeCun/Canziani)
NYU Deep Learning course (Spring 2021) by Yann LeCun and Alfredo Canziani, with lectures, labs and notebooks.
OECD AI Recommendation (OECD-LEGAL-0449)
by OECD
OECD Recommendation of the Council on Artificial Intelligence, the source of the OECD AI Principles and AI system definition.
PDE-Transformer
by TUM Physics-based Simulation
PDE-Transformer project page: transformer architecture for learning partial differential equations from data.
Physics-based Deep Learning (TUM)
by Nils Thuerey et al. (TU Munich)
Physics-based Deep Learning book from TUM: hands-on guide to combining physical simulation with deep learning.
PointNet
by Charles R. Qi et al. (Stanford University)
PointNet project page: deep network architecture that consumes raw point clouds for 3D classification and segmentation.
Roboflow documentation
by Roboflow
Roboflow documentation for building computer-vision datasets, training models and deploying them.
sktime
by sktime
sktime: open-source Python framework for time-series forecasting, classification and anomaly detection.
Soniox Docs: Models
by Soniox
Soniox models page listing speech-to-text model versions, changelog and deprecations.
Speechmatics technical articles
by Speechmatics
Speechmatics articles on speech recognition technology, product news and AI research.
UvA Deep Learning tutorials
by Phillip Lippe (University of Amsterdam)
University of Amsterdam deep learning tutorial notebooks in PyTorch and JAX/Flax, covering core architectures.
XGBoost documentation
XGBoost documentation: distributed gradient boosting library with training, GPU support and parameter guides.
YSDA Practical Deep Learning
by Yandex School of Data Analysis
YSDA Practical_DL GitHub repo: deep learning course materials from Yandex School of Data Analysis, HSE and Skoltech.
YSDA Practical RL
by Yandex School of Data Analysis
YSDA Practical_RL GitHub repo: open reinforcement-learning course with notebooks and assignments from HSE and YSDA.
Zoom Scribe API documentation
by Zoom
Zoom Scribe API documentation: transcription service for many media formats, with modes and authentication details.