The New York Institute of Finance (NYIF) and Google Cloud have launched a new Machine Learning for Trading Specialisation available exclusively on the Coursera platform. The Specialisation helps learners leverage the latest AI and machine learning techniques for financial trading. Amid the Fourth Industrial Revolution, nearly 80 per cent of financial institutions cite machine learning as a core component of business strategy and 75 per cent of financial services firms report investing significantly in machine learning. The Machine Learning for Trading Specialisation equips professionals with key technical skills increasingly needed in the financial industry today. Composed of three courses in financial trading, machine learning, and artificial intelligence, the Specialisation features a blend of theoretical and applied learning.

The New York Institute of Finance (NYIF) and Google Cloud announced a new Machine Learning for Trading Specialization available exclusively on the Coursera platform. The Specialization helps learners leverage the latest AI and machine learning techniques for financial trading. Amid the Fourth Industrial Revolution, nearly 80 percent of financial institutions cite machine learning as a core component of business strategy and 75 percent of financial services firms report investing significantly in machine learning. The Machine Learning for Trading Specialization equips professionals with key technical skills increasingly needed in the financial industry today. Composed of three courses in financial trading, machine learning, and artificial intelligence, the Specialization features a blend of theoretical and applied learning.

Researchers demo how deep neural networks can be trained to generate passwords better than the best password-cracking tools. Researchers at the Stevens Institute of Technology in New York, and the New York Institute of Technology have devised what they claim is a highly effective way to guess passwords using a deep learning tool called Generative Adversarial Networks (GANs). Tests of the'PassGAN' technique, as the researchers are calling it, show the method to be an improvement over state-of-the-art, rules-based password guessing tools such as HashCat and John the Ripper, the researchers said in a recently published technical paper. In their experiments the researchers were able to match nearly 47% - or some 2,774,269 out of 5,919,936 passwords - from a testing set comprised of real user passwords that were publicly leaked after a 2010 data breach at RockYou. Overall, the evaluations showed PassGAN outperforming John the Ripper by a factor of two, and being at least as competitive with passwords generated using the best rules from HashCat. When the output from PassGAN was combined with HashCat output the researchers could match about 24% more passwords than generated by HashCat alone.

Researchers at the Stevens Institute of Technology in New York, and the New York Institute of Technology have devised what they claim is a highly effective way to guess passwords using a deep learning tool called Generative Adversarial Networks (GANs). Tests of the'PassGAN' technique, as the researchers are calling it, show the method to be an improvement over state-of-the-art, rules-based password guessing tools such as HashCat and John the Ripper, the researchers said in a recently published technical paper. In their experiments the researchers were able to match nearly 47% -- or some 2,774,269 out of 5,919,936 passwords -- from a testing set comprised of real user passwords that were publicly leaked after a 2010 data breach at RockYou. Overall, the evaluations showed PassGAN outperforming John the Ripper by a factor of two, and being at least as competitive with passwords generated using the best rules from HashCat. When the output from PassGAN was combined with HashCat output the researchers could match about 24% more passwords than generated by HashCat alone.