The project will be updated continuously ...... Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength. Because of these different frameworks, the awesome convertors of deep learning models for different frameworks occur. It should be noted that I did not test all the converters, so I could not guarantee that each was available. But I also hope this convertor collection may help you!

The following is a guest contributed post from Jeremy Fain, CEO and founder of Cognitiv. A lot of people have asked me about my thoughts for 2018, and what I think the overarching trends in advertising technology and deep learning will be in the coming year. Far be it from me to disappoint my fans – so here's my take on next year's biggest topics: AI has been a buzzword for the past several years, as clearly evidenced by the vast numbers of products claiming to be AI currently on the market. While most of these applications don't really live up to the picture of AI that most people have in their heads of a Jetsons-like robot, there have lately been a series of discoveries, most notably in the field of deep learning, that are sure to have a serious impact on the way that most businesses operate. Deep learning and neural networks are at the heart of some of the most astonishing machine learning developments, from image recognition to the natural language processing that enables gadgets like Amazon's Alexa and Google Home to operate.

Alibaba says its deep neural network model has outscored humans in a global reading test, paving the way for the underlying technology to reduce the need for human input. The Chinese tech giant's research unit, Institute of Data Science of Technologies (IDST), said it had developed a deep-learning model that attained a score of 82.44 in Exact Match on the Stanford Question Answering Dataset (Squad). Humans had clocked a previous score of 82.304, it said. Squad comprised more than 100,000 question-and-answer sets based on more than 500 Wikipedia articles, in which participants were required to build machine-learning models to respond to the questions. These models would be evaluated by Squad, which then would run the model on the test set.

What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.

For each generated word, the model pays attention to specific words of the input and the previously generated output. Automatic summarization models can work in one of two ways: by extraction or by abstraction. Extractive models perform "copy-and-paste" operations: they select relevant phrases of the input document and concatenate them to form a summary. They are quite robust since they use existing natural-language phrases that are taken straight from the input, but they lack in flexibility since they cannot use novel words or connectors. They also cannot paraphrase like people sometimes do.

Currently, many of us are overwhelmed with mighty power of Deep Learning. We start to forget about humble graphical models. CRF is not so trendy as LSTM, but it is robust, reliable and worth noting. In this post, you will find a short summary about CRF (aka Conditional Random Fields) – what is this thing, what is it for and some interesting facts. In practical implementation, the computational time is often larger due to many other operations like numerical scaling, smoothing etc.

First, it was the AlphaGo AI from Google's DeepMind subsidiary which beat the world's best Go players at their own game to make a record. Then, an AI named Libratus, developed by the Carnegie Mellon University, outclassed Poker pros in a tournament to turn the world's attention towards the rapid pace at which AI is progressing. In the latest such example of an AI outsmarting human beings, a deep neural network model developed by Alibaba fared better than humans in a reading comprehension test. The AI model developed by Alibaba's Institute of Data Science and Technologies blazed past the SQuAD (Stanford Question Answering Dataset) test- one of the most reliable reading comprehension test for evaluating a machine's language skills- in a contest which pitted it against human rivals. Alibaba's AI scored a cumulative 82.44 Exact Match (EM) points, outscoring its human competitors who manged to put up 82.304 points on the scoreboard.

The folks at DeepMind are seeking to contribute to AI safety. They have designed a 2D testing environment for algorithms. The environment does not purport to cover all possible AI safety problems. For example, interpretability, multi-agent and formal verification safety problems are not covered. But a decent number is covered.

John Young (left) and Gus Grissom flew on the first crewed Gemini flight, Gemini 3, on March 23, 1965. Here, they're shown in the spacecraft simulator at the McDonnell plant in St. Louis. One additional "passenger" on the real flight was a corned-beef sandwich that Young smuggled aboard in his pocket. While John Young, who died on Jan. 5 at age 87, is famous for his Apollo 16 moonwalks and his role as commander of the first space shuttle mission, the NASA astronaut is also remembered for a small scandal he triggered with a sneaky act: smuggling a corned-beef sandwich into space. Young slipped the sandwich into his pocket just before launching on Gemini 3 on March 23, 1965.

The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). Their survey included a variety of questions about data science, machine learning, education and more.