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Deep Learning course at NYU, Spring 2020. Taught by Yann LeCun & Alfredo Canziani. With practical applications using PyTorch. Course website: http://bit.ly/p...
Complete course on Deep Learning, with all the material available on line including lecture and practicum videos, slide decks, homeworks, Jupyter notebooks, and transcripts in several languages.
People also ask
What is deep learning & representation learning?
Where can I find a course on deep learning?
What are the prerequisites for deep learning?
- Theme 1: Introduction
- Theme 2: Parameters Sharing
- Theme 3: Energy Based Models, Foundations
- Theme 4: Energy Based Models, Advanced
- Theme 5: Associative Memories
- Theme 6: Graphs
- Theme 7: Control
- Theme 8: Optimisation
- Miscellaneous
History and resources 🎥 🖥Gradient descent and the backpropagation algorithm 🎥 🖥Neural nets inference 🎥 📓Modules and architectures 🎥 🖥Recurrent and convolutional nets 🎥 🖥 📝ConvNets in practice 🎥 🖥 📝Natural signals properties and the convolution 🎥 🖥 📓Recurrent neural networks, vanilla and gated (LSTM) 🎥 🖥 📓 📓Energy based models (I) 🎥 🖥Inference for LV-EBMs 🎥 🖥What are EBMs good for? 🎥Energy based models (II) 🎥 🖥 📝Energy based models (III) 🎥 🖥Unsup learning and autoencoders 🎥 🖥Energy based models (VI) 🎥 🖥From LV-EBM to target prop to (any) autoencoder 🎥 🖥Energy based models (V) 🎥 🖥Attention & transformer 🎥 🖥 📓Graph transformer nets [A][B] 🎥 🖥Graph convolutional nets (I) [from last year] 🎥 🖥Graph convolutional nets (II) 🎥 🖥 📓Planning and control 🎥 🖥The Truck Backer-Upper 🎥 🖥 📓Prediction and Planning Under Uncertainty 🎥 🖥Optimisation (I) [from last year] 🎥 🖥Optimisation (II) 🎥 🖥 📝SSL for vision [A][B] 🎥 🖥Low resource machine translation [A][B] 🎥 🖥Lagrangian backprop, final project, and Q&A 🎥 🖥 📝May 2, 2024 · Yann LeCun’s Deep Learning Course covers the latest techniques in both deep learning and representation learning, focusing on supervised/self-supervised learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
MonmondayTuetuesdayWedwednesdayThuthursday29April 29, 202430April 30, 2024May1May 1, 20242May 2, 2024●(1 event) Category: General ...6May 6, 2024●(1 event) Category: General ...7May 7, 20248May 8, 20249May 9, 2024●(1 event) Category: General ...13May 13, 2024●(1 event) Category: ...14May 14, 202415May 15, 202416May 16, 202420May 20, 202421May 21, 202422May 22, 202423May 23, 2024This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
Ads
related to: yann lecun deep learning courseGain The Knowledge & Skills To Begin Or Advance Your Career. Call Us Now. We Continue To Invest In Academic Programs That Meet Workforce Needs. Call Us.
Build expertise in AI & Machine Learning in 6 months. Learn from faculty at UT Austin. Work on real-world projects under the guidance of industry experts.
Build Foundational Knowledge of Generative AI, Including LLMs, With 4 Short Videos. Start Training With Databricks Today. Just Watch 4 Short Videos and Pass a Knowledge Test.
