Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix typo and two practical book suggestions #2

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

fix typo and two practical book suggestions #2

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 6 additions & 4 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,10 @@ I find that the key of intelligence and cognition is a very interesting subject
## Books

- [How to Create a Mind](https://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/B009VSFXZ4) - The audio version is nice to listen to while commuting. This book is motivating about reverse-engineering the mind and thinking on how to code AI.
- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) - This book covers many of the core concepts behind neural networks and deep learning.
- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) - This book covers many of the core concepts behind neural networks and deep learning.
- [Deep Learning - An MIT Press book](http://www.deeplearningbook.org/) - Yet halfway through the book, it contains satisfying math content on how to think about actual deep learning.
- [Deep Learning with Python](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/) - Introduction to Deep Learning in Python from the creator of Keras.
- [Deep Learning and the Game of Go](https://www.manning.com/books/deep-learning-and-the-game-of-go) - Practical introduction to Deep Learning by learning how to create an advanced Go bot.
- [Some other books I have read](https://books.google.ca/books?hl=en&as_coll=4&num=10&uid=103409002069648430166&source=gbs_slider_cls_metadata_4_mylibrary_title) - Some books listed here are less related to deep learning but are still somehow relevant to this list.

<a name="posts-and-articles" />
Expand Down Expand Up @@ -92,7 +94,7 @@ I find that the key of intelligence and cognition is a very interesting subject
### Librairies and Implementations
- [TensorFlow's GitHub repository](https://github.com/tensorflow/tensorflow) - Most known deep learning framework, both high-level and low-level while staying flexible.
- [skflow](https://github.com/tensorflow/skflow) - TensorFlow wrapper à la scikit-learn.
- [Keras](https://keras.io/) - Keras is another intersting deep learning framework like TensorFlow, it is mostly high-level.
- [Keras](https://keras.io/) - Keras is another interesting deep learning framework like TensorFlow, it is mostly high-level.
- [carpedm20's repositories](https://github.com/carpedm20) - Many interesting neural network architectures are implemented by the Korean guy Taehoon Kim, A.K.A. carpedm20.
- [carpedm20/NTM-tensorflow](https://github.com/carpedm20/NTM-tensorflow) - Neural Turing Machine TensorFlow implementation.
- [Deep learning for lazybones](http://oduerr.github.io/blog/2016/04/06/Deep-Learning_for_lazybones) - Transfer learning tutorial in TensorFlow for vision from high-level embeddings of a pretrained CNN, AlexNet 2012.
Expand Down Expand Up @@ -191,14 +193,14 @@ Okay, signal processing might not be directly related to deep learning, but stud
- [Going Deeper with Convolutions](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) - GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.
- [Highway Networks](https://arxiv.org/pdf/1505.00387v2.pdf) - Highway networks: residual connections.
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/pdf/1502.03167v3.pdf) - Batch normalization (BN): to normalize a layer's output by also summing over the entire batch, and then performing a linear rescaling and shifting of a certain trainable amount.
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) - The U-Net is an encoder-decoder CNN that also has skip-connections, good for image segmentation at a per-pixel level.
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) - The U-Net is an encoder-decoder CNN that also has skip-connections, good for image segmentation at a per-pixel level.
- [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385v1.pdf) - Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers and make any vision model work properly at recognition given enough data".
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/pdf/1602.07261v2.pdf) - For improving GoogLeNet with residual connections.
- [WaveNet: a Generative Model for Raw Audio](https://arxiv.org/pdf/1609.03499v2.pdf) - Epic raw voice/music generation with new architectures based on dilated causal convolutions to capture more audio length.
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://arxiv.org/pdf/1610.07584v2.pdf) - 3D-GANs for 3D model generation and fun 3D furniture arithmetics from embeddings (think like word2vec word arithmetics with 3D furniture representations).
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://research.fb.com/publications/ImageNet1kIn1h/) - Incredibly fast distributed training of a CNN.
- [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993.pdf) - Best Paper Award at CVPR 2017, yielding improvements on state-of-the-art performances on CIFAR-10, CIFAR-100 and SVHN datasets, this new neural network architecture is named DenseNet.
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/pdf/1611.09326.pdf) - Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/pdf/1611.09326.pdf) - Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.


<a name="youtube" />
Expand Down