https://2018.za.pycon.org/talks/68-guide-to-choose-right-deep-learning-framework-for-your-ai-project/

As the world evolves around artificial intelligence (AI), the demand for AI-based products has grown exponentially, and so has AI research. Deep learning algorithms and techniques are widely used for the research and development of these products. The good news is that, year after year, Deep Learning has seen its glory in the release of many open source frameworks that facilitate the development and implementation of these algorithms.

As there are many deep learning frameworks, it can be confusing as to which one is best for your task. And choosing a deep learning framework for an AI project is as important as choosing a programming language to code the product. A data science project coupled with the right deep learning framework truly amplified overall productivity.

In this talk, I will discuss the commonalities that help developers understand which framework will be best suited to solve given business challenges. We'll also look at some of the most widely used frameworks and compare them with standard benchmarks.

The following deep learning/machine learning frameworks will be covered: ‹br/›
1. **Tensorflow**‹br/›
2. **PyTorch**‹br/›
3. **Chainer** and/or **MXNET**‹br/›

Highlight of this conference:

* define the key points for judging any deep learning framework.

* Material dependencies.

* anatomy of widely used open source frameworks.

* comparison of the frameworks mentioned above according to the key points defined.

**Who is the audience?**

Anyone who has inspired the coding of deep learning algorithms.

**Public level**: Beginner to Intermediate

pyconza2018

python

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