# Learn Deep Learning with this Free Course from Yann LeCun

Here is a freely-available NYU course on deep learning to check out from Yann LeCun and Alfredo Canziani, including videos, slides, and other helpful resources.

It goes without saying that there are a nearly unlimited number of resources available for learning deep learning from scratch. This applies at least equally to freely-available resources, it not more so than paid. A course which has been on the community's radar recently and being shared widely across social media is the aptly titled **Deep Learning** course from the NYU Center for Data Science, taught by Yann LeCun & Alfredo Canziani.

Here is an overview of the course, directly from its website:

This 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. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

The course provides a balanced introduction to a wide variety of deep learning concepts. A non-exhaustive list of course lecture and practicum topics include:

- SGD and backprop
- Backprop in practice
- NN training
- Parameter transformation
- CNN, autograd
- CNN applications
- RNNs and attention
- Training RNNs
- Autoencoders
- Contrastive methods
- Regularised latent
- Training GANs
- Activations
- Losses
- DL for NLP
- Attention & transformer
- Structured Prediction
- Regularisation and Bayesian
- Inference for Latent-Variable EBMs
- Training Latent-Variable EBMs

The website also acts as an online text of sorts, with structured HTML course notes compiled by lecture and section readable in-place.

Course material also includes executable Jupyter Notebooks with PyTorch implementations of various course concepts, which can be accessed directly here.

The course's YouTube lectures playlist can be directly accessed here.

Impressively, translations of the course materials are presented in: English, Arabic, Spanish, Italian, Japanese, Korean, Turkish, Chinese, French, Persian, Russian, with plans to add Portuguese, Bengali, and Vietnamese. These translations have been facilitated by more than 470 volunteers from 17 time zones around the world.

The opportunity to learn deep learning from a renowned deep learning researcher and expert from a top-notch university (not to mention without cost) represent a fantastic opportunity for all. The effectiveness of Canziani's approachable and explanatory videos are also not to be overlooked, being substantial highlights of the course materials. I would encourage everyone to have a look for themselves to see if this course is for them.

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