[GitHub] eric-haibin-lin commented on a change in pull request #9535: Tutorials index page update
eric-haibin-lin commented on a change in pull request #9535: Tutorials index page update URL: https://github.com/apache/incubator-mxnet/pull/9535#discussion_r163709852 ## File path: docs/tutorials/index.md ## @@ -1,84 +1,319 @@ # Tutorials -## Gluon +MXNet has two primary high-level interfaces for its deep learning engine: the Gluon API and the Symbol API. Tutorials for each are provided below. -Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface. -Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency. +The difference between the two is an imperative versus symbolic programming style. Gluon makes it easy to prototype, build, and train deep learning models without sacrificing training speed by enabling both (1) intuitive imperative Python code development and (2) faster execution by automatically generating a symbolic execution graph using the hybridization feature. -This is a selected subset of Gluon tutorials that explain basic usage of Gluon and fundamental concepts in deep learning. For a comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see [gluon.mxnet.io](http://gluon.mxnet.io). +**TLDR**: If you are new to deep learning or MXNet, you should start with the Gluon tutorials. -### Basics +The Gluon and Symbol tutorials are in Python, but you can also find a variety of other MXNet tutorials, such as R, Scala, and C++ in the [Other Languages API Tutorials](#other-mxnet-api-tutorials) section below. + +[Example scripts and applications](#example-scripts-and-applications) as well as [contribution](#contributing-tutorials) info is below. + + + + + + Python + + + + + Gluon + Symbol + + + + + + + Beginner + Intermediate + Advanced + + + + + + + + Data Loading + Basic Networks + Linear Regression + + + + + + + + Image Recognition + Human Language + Recommender Systems + Customization + + + + + + + + Distributed Training + Optimization + Adversarial Networks + + + + + + + + + + - [Manipulate data the MXNet way with ndarray](http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html) +- [Serialization - saving, loading and checkpointing](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html) +- [NDArray in Compressed Sparse Row Storage Format](http://mxnet.incubator.apache.org/tutorials/sparse/csr.html) +- [Sparse Gradient Updates](http://mxnet.incubator.apache.org/tutorials/sparse/row_sparse.html) + + + + + +- [Simple autograd example](http://mxnet.incubator.apache.org/tutorials/gluon/autograd.html) - [Automatic differentiation with autograd](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html) +- [Neural network building blocks with gluon](http://mxnet.incubator.apache.org/tutorials/gluon/gluon.html) +- [Hybrid network example](http://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html) + + + + - [Linear regression with gluon](http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html) -- [Serialization - saving, loading and checkpointing](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html) + + + + + + + -### Neural Networks + +- [Handwritten digit recognition (MNIST)](http://mxnet.incubator.apache.org/tutorials/gluon/mnist.html) - [Multilayer perceptrons in gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html) -- [Convolutional Neural Networks in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) -- [Recurrent Neural Networks with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) +- [Convolutional Neural Networks (CNNs) in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) +- [Multi-class object detection using CNNs in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) +- [Visual question answering in gluon](http://gluon.mxnet.io/chapter08_computer-vision/visual-question-answer.html) +- [Transferring knowledge through fine-tuning (not hotdog)](http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html) + -### Advanced -- [Plumbing: A look under the hood of gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html) + + +- [Simple Recurrent Neural Networks (RNNs) with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/simple-rnn.html) +- [Long Short-Term Memory (LSTM) RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/lstm-scratch.html) +- [Gated Recurrent Unit (GRU) RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/gru-scratch.html) +- [Advanced RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) +- [Tree LSTM modeling for semantic
[GitHub] eric-haibin-lin commented on a change in pull request #9535: Tutorials index page update
eric-haibin-lin commented on a change in pull request #9535: Tutorials index page update URL: https://github.com/apache/incubator-mxnet/pull/9535#discussion_r163709740 ## File path: docs/tutorials/index.md ## @@ -1,84 +1,319 @@ # Tutorials -## Gluon +MXNet has two primary high-level interfaces for its deep learning engine: the Gluon API and the Symbol API. Tutorials for each are provided below. -Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface. -Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency. +The difference between the two is an imperative versus symbolic programming style. Gluon makes it easy to prototype, build, and train deep learning models without sacrificing training speed by enabling both (1) intuitive imperative Python code development and (2) faster execution by automatically generating a symbolic execution graph using the hybridization feature. -This is a selected subset of Gluon tutorials that explain basic usage of Gluon and fundamental concepts in deep learning. For a comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see [gluon.mxnet.io](http://gluon.mxnet.io). +**TLDR**: If you are new to deep learning or MXNet, you should start with the Gluon tutorials. -### Basics +The Gluon and Symbol tutorials are in Python, but you can also find a variety of other MXNet tutorials, such as R, Scala, and C++ in the [Other Languages API Tutorials](#other-mxnet-api-tutorials) section below. + +[Example scripts and applications](#example-scripts-and-applications) as well as [contribution](#contributing-tutorials) info is below. + + + + + + Python + + + + + Gluon + Symbol + + + + + + + Beginner + Intermediate + Advanced + + + + + + + + Data Loading + Basic Networks + Linear Regression + + + + + + + + Image Recognition + Human Language + Recommender Systems + Customization + + + + + + + + Distributed Training + Optimization + Adversarial Networks + + + + + + + + + + - [Manipulate data the MXNet way with ndarray](http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html) +- [Serialization - saving, loading and checkpointing](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html) +- [NDArray in Compressed Sparse Row Storage Format](http://mxnet.incubator.apache.org/tutorials/sparse/csr.html) +- [Sparse Gradient Updates](http://mxnet.incubator.apache.org/tutorials/sparse/row_sparse.html) + + + + + +- [Simple autograd example](http://mxnet.incubator.apache.org/tutorials/gluon/autograd.html) - [Automatic differentiation with autograd](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html) +- [Neural network building blocks with gluon](http://mxnet.incubator.apache.org/tutorials/gluon/gluon.html) +- [Hybrid network example](http://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html) + + + + - [Linear regression with gluon](http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html) -- [Serialization - saving, loading and checkpointing](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html) + + + + + + + -### Neural Networks + +- [Handwritten digit recognition (MNIST)](http://mxnet.incubator.apache.org/tutorials/gluon/mnist.html) - [Multilayer perceptrons in gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html) -- [Convolutional Neural Networks in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) -- [Recurrent Neural Networks with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) +- [Convolutional Neural Networks (CNNs) in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) +- [Multi-class object detection using CNNs in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) +- [Visual question answering in gluon](http://gluon.mxnet.io/chapter08_computer-vision/visual-question-answer.html) +- [Transferring knowledge through fine-tuning (not hotdog)](http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html) + -### Advanced -- [Plumbing: A look under the hood of gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html) + + +- [Simple Recurrent Neural Networks (RNNs) with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/simple-rnn.html) +- [Long Short-Term Memory (LSTM) RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/lstm-scratch.html) +- [Gated Recurrent Unit (GRU) RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/gru-scratch.html) +- [Advanced RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) +- [Tree LSTM modeling for semantic
