Discover the framework and other tools available in the deep learning ecosystem. Each framework and api got his own example.

1. Machine learning from
Software Developers point
of view
Let’s dive into the A.I jungle !
@antoined
@pierre_paci
Antoine Detante
Pierre Paci

3. Framework
Dive in the jungle !

4. • Complete ecosystem
• Biggest community
• 2nd biggest code repository on GitHub
• Complete model zoo usable for production
• Developed and released by Google Brain
• Python, C++, Java, Rust, Haskell
• Close relation with Google Cloud ML
• Static graph computation
• New Dynamic mode since 1.5 : TensorFlow Eager
• Hard to escape TensorFlow ecosystem
• Raw TensorFlow can be difficult
• Describing the TF ecosystem would need
an entire presentation

7. • Lots of state of the art implementation
• Facebook publish lots of model
• Only one simple API
• Learn it once and for all
• Part of the ONNX ecosystem
• Very quick expansion
• Developed and released by Facebook Research
• Python
• Fork from Lua’s Torch framework
• Lots of official paper implementation released in PyTorch
• Dynamic graph computation
• Deployment
• Must have a complete Python pipeline
• Must use ONNX and another
framework
• No direct cloud support
• Quite new

9. A word on Caffe2
How is Caffe2 different from PyTorch?
Caffe2 is built to excel at mobile and at large scale deployments. While it is new in Caffe2 to support
multi-GPU, bringing Torch and Caffe2 together with the same level of GPU support, Caffe2 is built to
excel at utilizing both multiple GPUs on a single-host and multiple hosts with GPUs.
PyTorch is great for research, experimentation and trying out exotic neural networks, while
Caffe2 is headed towards supporting more industrial-strength applications with a heavy focus
on mobile.
This is not to say that PyTorch doesn’t do mobile or doesn’t scale or that you can’t use Caffe2 with
some awesome new paradigm of neural network, we’re just highlighting some of the current
characteristics and directions for these two projects. We plan to have plenty of interoperability and
methods of converting back and forth so you can experience the best of both worlds.

10. • Apache project
• Low, high level API (Gluon)
• ONNX Support
• Industry ready
• Fit for research and production
• Apache Project
• Currently, MXNet is supported by Intel, Dato, Baidu, Microsoft, Wolfram Research,
and research institutions such as Carnegie Mellon, MIT, the University of
Washington, and the Hong Kong University of Science and Technology.
• Supported on AWS and Azure
• Designed for Big scale
• Portable
• Nearly all language with binding
• C++ binary compilation for all platform (mobile included)
• Static and dynamic graph computation
• Small model zoo
• Small community
• But big industry support

12. High level API
And cross API

13. • Super easy to learn
• Can scale to more complex problem
• Lots of helpers included
• Integrated model zoo
• Integration with scikit-learn
• Initially a high level interface to Theano and Tensorflow
• Now officially part of Tensorflow
• Started by François Chollet, from Google
• Focus on quick iteration
• Behave like a complete framework
• No real company behind it
• Model zoo is lacking state of the art

16. • Apache projet
• Low, high level API (Gluon)
• ONNX Support
• Industry ready
• Fit for research and production
• Developed by MXNet
• Inspired by PyTorch
• More adapted to Research or Dynamic graph computation than raw MXNet
• Should be supported by CNTK (Microsoft) soon
• Small model zoo
• Small community
• But big industry support

18. Common model format
How to switch

19. About ONNX
ONNX is a community project created by Facebook and Microsoft. We believe there is a need for
greater interoperability in the AI tools community. Many people are working on great tools, but
developers are often locked in to one framework or ecosystem.
ONNX provides a definition of an extensible computation graph model, as well as definitions of built-
in operators and standard data types.
Operators are implemented externally to the graph, but the set of built-in operators are portable
across frameworks. Every framework supporting ONNX will provide implementations of these
operators on the applicable data types.

20. Global overview
Map of the ecosystem

21. ONNX
Converter
Soon

22. Let’s dive into
Neural networks API for human beings

23. An Open Source neural networks library
• Written in Python
• Running on top of TensorFlow, CNTK & Theano
• Can be run on CPU and GPU
• Supports CNN and RNN, as well as combinations of the two
…built for fast experimentation
• User friendliness: designed for human beings, not machines! Consistent and simple API
• Modularity: models are sequences or graphs of standalone modules that can be plugged
together
• Extensibility: new modules are simple to add (as new classes and functions)
• Work with Python: models are described in Python code and are compact, easy to debug and
easy to extend

24. Sequential model

25. Keras has a lot of built in layers
• Dense layer of neural network
• Common activation functions like linear, sigmoid, tanh, ReLU, …
• Dropout, L1/L2 regularizers
• Convolutional layers (Conv1D, Conv2D, Conv3D, …)
• Pooling layers
• Recurrent layers (fully connected RNN, LTSM, …)
All these layers can be tuned, and you can add custom layers by extending existing ones or writing
new Python classes.

26. Compilation

27. Custom optimizer, loss and metrics

28. Training the model
• Models are trained on Numpy arrays
• Input data and labels must be passed to the fit method of the model
• The number of epochs is fixed (number of iterations on the dataset)
• Validation set can be provided to the fit method (for evaluation of loss and metrics)
At the end of the training, fit will return an history of metrics and training loss values at each epochs.

29. Training the model

30. Trained model

31. Integration with
Scikit-Learn
Keras provides wrappers which
can be used from Scikit-Learn
pipelines.
It allows to use a Keras
Sequential model as a classifier
or regressor in Scikit-Learn

32. Keras functional API
The Keras functional API is the
way to go for defining complex
models, such as multi-output
models, directed acyclic
graphs, or models with shared
layers.
With the functional API, all
models can be called as if it
where a layer. It’s easy to
reused trained models in a
larger pipeline.

33. Graphs with multiple input / output