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Alessandro Pedori
Alessandro is a full-stack AI, NLP, and ML engineer, consultant, 他是一名架构师,有几年教人工智能做什么和围绕它构建代码的经验, mainly in Python. He also loves helping people understand the limits of current systems, triaging what to implement first, building code, teaching, consulting, mentoring, and coaching. 最重要的是,亚历山德罗热衷于技术如何使人类发光发热.
Show MorePavlo Karalupov
Pavlo is a full-stack developer with six years of experience. This includes a software development internship at Amazon Web Services, focused on Amazon Transcribe, a deep-learning, text-to-speech application; a machine learning internship at Swisscom TV, focused on code-switching; and several small startups, including one that has over 2.5 million users across 250 hospitals. Pavlo拥有计算机科学硕士学位,专注于机器学习,这为他的行业经验提供了支持.
Show MoreThierno Ibrahima Diop
Thierno是一位首席数据科学家,对自然语言处理(NLP)和机器学习(ML)充满热情。. He has been mentoring data scientist apprentices for three years. 他之前在网络和移动应用程序开发方面做了三年的自由职业者. Thierno is co-founder of GalsenAI, an artificial intelligence (AI) community in Senegal, a Coursera instructor on data science, and a Google developer expert in ML.
Show MoreYerlan Temir
Yerlan是一位才华横溢的全栈工程师,拥有前端和后端项目经验. 他有四年的软件工程经验,使用React和Node.js with TypeScript to build web applications. Besides being proficient in different technologies, programming languages, and frameworks, 叶兰有领导团队和进行代码和设计审查的经验. He always strives to provide high-quality solutions, driving business value while complying with code best practices.
Show MoreDanilo Onishi
Danilo是一名机器学习工程师,拥有名古屋工业大学(Nagoya Institute of Technology)计算机科学硕士学位,在自动语音识别(ASR)领域有7年以上的经验。, natural language processing (NLP), and audio classification. 从研究到生产,他参与了机器学习生命周期的所有步骤. He also designs data pipelines and performs training, evaluation, 为教育和商业智能应用部署深度学习模型.
Show MoreLasse Hyyrynen
Lasse is a data scientist with a background in mathematics. He excels in Natural Language Processing (NLP), data modeling, machine learning, Python, and artificial intelligence projects. 他致力于探索ML和AI的广泛可能性的现实问题. Lasse has developed ML models that detect cybersecurity threats, created processing pipelines for machine learning models, 通过增强外来词的发音模型,提高了语音识别的准确率.
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