In this article, you learn how to do algebraic mathematics computation in Python with SymPy module.
If I had to pick one platform that has single-handedly kept me up-to-date with the latest developments in data science and machine learning – it would be GitHub. The sheer scale of GitHub, combined with the power of super data scientists from all over the globe, make it a must-use platform for anyone interested in this field. Can you imagine a world where machine learning libraries and frameworks like BERT, StanfordNLP, TensorFlow, PyTorch, etc. weren’t open sourced? It’s unthinkable! GitHub has democratized machine learning for the masses – exactly in line with what we at Analytics Vidhya believe in. This was one of the primary reasons we started this GitHub series covering the most useful machine learning libraries and packages back in January 2018.
According to recently released survey data that was collected in November 2018, European trust in the Internet is at its lowest in a decade. These results show that the General Data Protection Regulation (GDPR) – which the EU has touted as the gold standard for data protection rules – has had no impact on consumer trust in the digital economy since it came into force last May. Moreover, these findings suggest that the conventional wisdom among EU policymakers – that more regulation is necessary to spur consumer trust and innovation in the digital economy – is fundamentally flawed and should be abandoned. According to the Eurobarometer, a series of regular public opinion polls conducted on behalf of the European Commission, online consumer trust has declined in Europe over the past year. In November 2018 – six months after the GDPR went into effect – only 32 percent of EU respondents indicated that they ‘tend to trust’ the Internet, down 2 percentage points from a year earlier. As shown in Figure 1, this was the lowest level of consumer trust in over a decade. These findings suggest that the EU’s approach to regulating the digital economy has been largely ineffective in achieving one of its primary goals.
Four years ago, the software engineer Jack Alciné caused a storm by pointing out to Google that their algorithm had the unsavoury tendency to classify his black friends as Gorillas. Following a public outcry for blatant racism, the giant apologised and diligently ‘fixed’ the problem. Last year Amazon got into hot water by finding its advanced AI hiring software heavily favoured men for technical positions. Again, retraction followed the outcry. In a more newsworthy style, an unfortunate translation from Facebook accidentally got a Palestinian man arrested in Israel by mis-translating a caption he had posted on a photo of himself. Posing next to a bulldozer, the caption read ‘Attack them!’ instead of ‘Good morning!’. The man underwent questioning for several hours until the mistake came to light. But the GAFA aren’t the only ones struggling to navigate the dangers of at-scale AI and one can easily find a plethora of examples of discriminatory data science. Take the work coming out of the MIT Media Lab for example, where Joy Buolamwini[i] showed in early 2018 that three of the latest gender-recognition AIs, from IBM, Microsoft and Megvii, could infer a person’s gender from a photograph 99 per cent of the time as proclaimed… provided it was a white man. For dark-skinned women, accuracy dropped to a mere 35 per cent. You can imagine their public relations troubles.
The goal of reinforcement learning (RL) is to train smart agents that can interact with their environment and solve complex tasks, with real-world applications towards robotics, self-driving cars, and more. The rapid progress in this field has been fueled by making agents play games such as the iconic Atari console games, the ancient game of Go, or professionally played video games like Dota 2 or Starcraft 2, all of which provide challenging environments where new algorithms and ideas can be quickly tested in a safe and reproducible manner. The game of football is particularly challenging for RL, as it requires a natural balance between short-term control, learned concepts, such as passing, and high level strategy.
An intuitive explanation of the Recurrent Neural Network , LSTM and GRU
Humanizing Emotion Classification with a Deep Neural Network that Captures Multimodal Contextual Information