The U.S. Bureau of Labor statistics estimates that 11.5 million new data science jobs will be added by 2026. This projection suggests that the field of data science will continue to rapidly grow with the high demand for these positions in industry. For those just starting out in the space or considering a transition from another industry, podcasts are useful for developing an understanding of data science and machine learning in industry and research. In this post, I will discuss three of my favorite podcasts that discuss the data science field in research and industry. These podcasts also suggest learning resources for those how are just starting out.
美国劳工统计局估计，到2026年将增加1150万个新的数据科学工作。这一预测表明，随着对这些职位的高度需求，数据科学领域将继续快速增长。 对于刚开始涉足这一领域或正在考虑从另一个行业过渡的人们，播客可用于增进对行业和研究中的数据科学和机器学习的理解。 在这篇文章中，我将讨论我最喜欢的三个播客，它们讨论研究和行业中的数据科学领域。 这些播客还为那些刚刚起步的人提供了学习资源。
Let’s get started!
让我们开始吧！
超级数据科学 (SuperDataScience)
The SuperDataScience podcast is hosted by Kirill Eremenko, who is a data science coach and entrepreneur. SuperDataScience features many leading data scientists and data analysts who provide insights into how to establish a successful career in data science. This podcast is a must for anyone who wants to better understand the industry and continue to educate themselves in the field. One of my favorites is SDS 391: Data Science Campfire Tales with John Elder. In this episode, Kirill and John Elder discuss mathematical concepts such as calculus, statistics and resampling. They also discuss the importance of domain knowledge, thoughts on neural networks, thoughts on the future of data science and much more. I also enjoyed SDS 373: TensorFlow and AI Learning for Developers. Here, Kirill sits down with Laurence Moroney to discuss TensorFlow and how developers’ can use it to progress their careers in data science. I also recommend SDS 379: Maelstrom, Chaos, and Mayhem: Guiding Your Data Science Career Path. Here, Kirill sits down with Christopher Bishop who has outlined a framework to help those starting out in data science identify their passion in the field.
SuperDataScience播客由数据科学教练兼企业家Kirill Eremenko主持。 SuperDataScience拥有许多领先的数据科学家和数据分析师，他们为如何建立成功的数据科学事业提供了见识。 对于任何想更好地了解该行业并继续在该领域进行自我教育的人，此播客都是必须的。 我的最爱之一是SDS 391：与John Elder的数据科学营火故事。 在本集中，基里尔和约翰·埃尔德讨论了数学概念，例如微积分，统计和重采样。 他们还讨论了领域知识的重要性，关于神经网络的思想，关于数据科学的未来的思想等等。 我还喜欢SDS 373：TensorFlow和面向开发人员的AI学习 。 在这里，Kirill与Laurence Moroney坐下来讨论TensorFlow以及开发人员如何使用它来发展数据科学事业。 我还建议使用SDS 379：Maelstrom，Chaos和Mayhem：指导您的数据科学职业道路 。 在这里，基里尔与克里斯托弗·毕晓普(Christopher Bishop)坐下来，后者概述了一个框架，以帮助那些从数据科学起步的人确定他们对该领域的热情。
Lex Fridman播客 (Lex Fridman Podcast)
In this podcast, MIT AI researcher Lex Fridman has conversations about AI, science, technology and more. Specifically, much of the content revolves around deep learning, AI robotics, computer vision, artificial general intelligence, computer science, and neuroscience. In contrast to the SuperDataScience podcast, this podcast focuses much more heavily on broad concepts such as intelligence and consciousness. This is a great podcast for those interested in the broad applications of machine learning methods as they are employed in artificial intelligence systems as well as those interested in how the brain works. I highly recommend #106 — Matt Botvinick: Neuroscience, and AI at DeepMind, where Fridman sits down with Matt Botvinick to discuss neuronal mechanisms of the brain as it relates to learning. I also recommend #101 — Joscha Bach: Artificial Consciousness and the Nature of Reality, where Fridman and Bach discuss the workings of the human brain, autonomous robots, discontinuity of existence, and many more interesting philosophical musings. Finally, I recommend #81 — Anca Dragan: Human-Robot Interaction and Reward engineering. On this podcast, Fridman and Anca discuss how robots as agents in the world perform tasks while navigating objects and people.
在此播客中，麻省理工学院AI研究人员Lex Fridman进行了有关AI，科学，技术等方面的对话。 具体而言，许多内容围绕着深度学习，人工智能机器人技术，计算机视觉，人工智能，计算机科学和神经科学展开。 与SuperDataScience播客相反，此播客将重点更多地放在诸如智能和意识之类的广泛概念上。 对于那些对在人工智能系统中使用机器学习方法的广泛应用感兴趣的人以及对大脑工作方式感兴趣的人，这是一个很棒的播客。 我强烈推荐＃106-Matt Botvinick：DeepMind的神经科学和AI ，Fridman和Matt Botvinick坐下来讨论与学习相关的大脑神经元机制。 我还建议＃101 — Joscha Bach：人工意识和现实的本质 ，Fridman和Bach在其中讨论了人脑的工作原理，自动机器人，存在的不连续性以及许多更有趣的哲学沉思。 最后，我推荐＃81 — Anca Dragan：人机交互和奖励工程 。 在此播客中，Fridman和Anca讨论了作为代理的机器人如何在导航对象和人员的同时执行任务。
数据怀疑者 (Data Skeptic)
The Data Skeptic podcast is hosted by Kyle Polich and it features interviews and discussions around data science, statistics, machine learning, and artificial intelligence. This podcast is much more research focused where many episodes involve discussions of recent papers in the space of machine learning. I recommend Interpretable AI in Healthcare, where Polich sits down with Jayaraman Thiagarajan to discuss his paper Calibrating Healthcare AI: Toward Reliable and Interpretable Deep Predictive models. In this podcast, Polich and Thiagarajan discuss formalizing the process of explaining model predictions in healthcare to doctors. I also enjoyed Self-Driving Cars and Pedestrians, where Polich sits down with Arash Kalatian to discuss how to decode pedestrian and automated vehicle interactions using virtual reality and interpretable deep learning. Finally, I recommend Adversarial Explanations, where Polich sits down with Walt Woods to discuss his paper Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness. Here, Polich and Woods talk about the concept of adversarial explanations which involve heat mapping at the intermediate layers of neural networks.
数据怀疑者播客由凯尔·波利奇(Kyle Polich)主持，其特色是围绕数据科学，统计，机器学习和人工智能进行访谈和讨论。 这个播客更多地集中在研究上，其中许多情节涉及机器学习领域中最近论文的讨论。 我建议在医疗保健中使用Interpretable AI ，Polich 在这里与Jayaraman Thiagarajan坐下来讨论他的论文“ 校准医疗保健AI：迈向可靠和可解释的深度预测模型” 。 在此播客中，Polich和Thiagarajan讨论了对医生解释医疗保健模型预测的过程的正式化。 我也很喜欢自动驾驶汽车和行人 ，Polich在这里与Arash Kalatian坐下来讨论如何使用虚拟现实和可解释的深度学习对行人和自动车辆交互进行解码。 最后，我推荐“ 对抗性解释” ，Polich与Walt Woods坐下来讨论他的论文“理解图像分类决策和改善的神经网络鲁棒性的对抗性解释” 。 在这里，Polich和Woods讨论了对抗性解释的概念，其中涉及神经网络中间层的热映射。
结论 (CONCLUSIONS)
To summarize, in this post we discussed three great podcasts that discuss machine learning and data science. First we discussed SuperDataScience, which focuses on career advice for budding data scientists. Next we talked about the Lex Fridman Podcast, which tackles many interesting questions around intelligence and consciousness. Finally, we discussed Data Skeptic, which features many discussion around frontline research in the space of machine learning. I hope you found this post interesting/useful. Thank you for reading!
总而言之，在本文中，我们讨论了讨论机器学习和数据科学的三个很棒的播客。 首先，我们讨论了SuperDataScience ，其重点是为新兴数据科学家提供的职业建议。 接下来，我们讨论了Lex Fridman播客 ，该播客解决了许多有关智力和意识的有趣问题。 最后，我们讨论了“ 数据怀疑论者” ，其中围绕机器学习领域的一线研究进行了许多讨论。 希望您觉得这篇文章有趣/有用。 感谢您的阅读！
翻译自: https://towardsdatascience.com/great-data-science-and-machine-learning-podcasts-211fefb80f07
