Python Libraries are a set of useful functions that eliminate the need for writing codes from scratch. There are over 137,000 python libraries present today, and they play a vital role in developing machine learning, data science, data visualization, image and data manipulation applications, and more. Let us briefly introduce Python Programming Language and then directly dive into the most popular Python libraries. Guido Van Rossum's brainchild – Python, which dates back to the '80s, has become an avid game changer. It is one of the most popular coding languages today and is widely used for a gamut of applications. So, how to make an app using Python? A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes instead of writing them from scratch every single time.

We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is the introduction of the intermediary modules into the current retriever-reader pipeline. Unlike previous methods that solely rely on the retriever for gathering all evidence in isolation, our intermediary performs a chain of reasoning over the retrieved set. Specifically, our method links the retrieved evidence with its related global context into graphs and organizes them into a candidate list of evidence chains. Built upon pretrained language models, our system achieves competitive performance on two ODQA datasets, OTT-QA and NQ, against tables and passages from Wikipedia. In particular, our model substantially outperforms the previous state-of-the-art on OTT-QA with an exact match score of 47.3 (45 % relative gain).

Machine learning is a broad topic. Deep learning, in particular, is a way of using neural networks for machine learning. Neural network is probably a concept older than machine learning, dated back to 1950s. Unsurprisingly, there were many libraries created for it. In the following, we will give an overview of some of the famous libraries for neural network and deep learning.

Working as a data scientist or data engineer, Python is a must-learn programming language. There is possibly no better way of learning Python than working on open-source projects. It will help you become skilled in the language better. Here are the top 10 Python AI open-source projects for you to try in 2022. Theano lets you optimize, evaluate, and define mathematical expressions that involve multi-dimensional arrays.

Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details about Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. Notice: As announced, Chainer is under the maintenance phase and further development will be limited to bug-fixes and maintenance only.

Preferred Networks is migrating its deep learning research platform from its own open source framework Chainer to PyTorch. The Japanese artificial intelligence startup unveiled the plan last week, assigning its new Chainer V7 to a "maintenance phase" in advance of the move. Preferred Networks will provide documentation and a library for Chainer users to facilitate the transition to PyTorch. According to a Nikkei survey, Preferred Networks ranks No.1 on estimated corporate value among 181 Japanese startups, with an estimated valuation of JP￥351.5 billion (US$3.24 Japanese auto maker Toyota has been working closely with Preferred Networks since its founding in 2014 and has pumped more than JP ¥11 billion (US$101 million) into the company's deep learning, robotics and self-driving R&D.

PFN to work with PyTorch and the open-source community to develop the framework and advance MN-Core processor support. Preferred Networks, Inc. (PFN, Head Office: Tokyo, President & CEO: Toru Nishikawa) today announced plans to incrementally transition its deep learning framework (a fundamental technology in research and development) from PFN's Chainer to PyTorch. Concurrently, PFN will collaborate with Facebook and the other contributors of the PyTorch community to actively participate in the development of PyTorch. With the latest major upgrade v7 released today, Chainer will move into a maintenance phase. PFN will provide documentation and a library to facilitate the migration to PyTorch for Chainer users.

Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.

Never would we have thought that characters designed by AI programs jumped from rudimentary to ultra-advanced in the space of three years. In 2015, an artificial intelligence program called Chainer was introduced to the world, which generated anime characters based on users' inputs and helped artists come up with their own ideas. It was relatively basic and created content that looked like it was haphazardly drawn. Nevertheless, it was a first attempt to design an AI that could create anime characters. But it became the stepping stone for a more sophisticated program featured on a website called MakeGirls.moe in 2017.

As we mentioned on our blog, Theano will stop development in a few weeks. Many aspects of Chainer were inspired by Theano's clean interface design, so we would like to introduce Chainer to users of Theano. We hope this article assists interested Theano users to move to Chainer easily. First, let's summarize the key similarities and differences between Theano and Chainer. In this post, we assume that the modules below have been imported.