As world rolling around Artificial Intelligence (AI), demand for the AI-based product seen exponential growth, so the AI research. Deep learning algorithms and techniques are widely used for research and development of these products. Good news is that year by year Deep Learning has seen its glory in the release of many open source frameworks which ease the pain to develop and implement these algorithms.
As there are many deep learning frameworks out there and it can lead to confusion as to which one is better for your task. And choosing a deep learning framework for an AI project is as important as choosing a programming language to code product, Data science project coupled with the right deep learning framework has truly amplified the overall productivity.
In this talk, I will discuss the common points which help developers to understand which framework will be the perfect fit for solving given business challenges. Also, we will look into some of the most widely used frameworks and comparing with standard benchmarks.
Following deep learning/machine learning frameworks will be discussed:
3. **Chainer** and/or **MXNET**
Key Highlight of this talk:
* define key points to judge any deep learning framework.
* hardware dependencies.
* anatomy of widely used open source frameworks.
* comparison of above-mentioned frameworks as per defined key points.
**Who is the audience?**
Anyone who inspired to code deep learning algorithms.
**Audience Level**: Beginner to Intermediate