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          Developing Universal Windows app using WinML model exported from Custom Vision      Cache   Translate Page   Web Page Cache   
[日本語版はこちら] Cognitive Services Custom Vision service is pre-build and customizable image classification & now object detection machine learning models builder, just uploading some photos to detect. You can use your ML models as Web API, also download in several format to use in your app, like CoreML for iOS, TensorFlow for Android, ONNX-based WinML for Windows, and now Docker...
          1時間6.5ドルでAIを開発できる時代へ――TPUをクラウドで提供したGoogleの真意      Cache   Translate Page   Web Page Cache   
Googleは2018年2月から、同社の機械学習用ライブラリ「TensorFlow」の処理を高速化するプロセッサ「TPU」のクラウド版「Cloud TPU(ベータ版)」の公開に踏み切った。「TPUは外販しない。あくまでクラウドで提供する」と同社が強調する理由とは一体何か。
          1時間6.5ドルでAIを開発できる時代へ――TPUをクラウドで提供したGoogleの真意      Cache   Translate Page   Web Page Cache   

Googleは2018年2月から、同社の機械学習用ライブラリ「TensorFlow」の処理を高速化するプロセッサ「TPU」のクラウド版「Cloud TPU(ベータ版)」の公開に踏み切った。「TPUは外販しない。あくまでクラウドで提供する」と同社が強調する理由とは一体何か。


          1時間6.5ドルでAIを開発できる時代へ――TPUをクラウドで提供したGoogleの真意      Cache   Translate Page   Web Page Cache   
Googleは2018年2月から、同社の機械学習用ライブラリ「TensorFlow」の処理を高速化するプロセッサ「TPU」のクラウド版「Cloud TPU(ベータ版)」の公開に踏み切った。「TPUは外販しない。あくまでクラウドで提供する」と同社が強調する理由とは一体何か。
          Ingénieur en apprentissage automatique - Groom & Associates - Montréal, QC      Cache   Translate Page   Web Page Cache   
Expérience avec tensorflow ou d'autres backends, keras ou autres frameworks, scikit-learn, OpenCV, Pandas. Experience with tensorflow or other backends, keras...
From Groom & Associates - Thu, 07 Jun 2018 17:10:46 GMT - View all Montréal, QC jobs
          Machine Learning/AI Engineer - Groom & Associates - Montréal, QC      Cache   Translate Page   Web Page Cache   
Expérience avec tensorflow ou d'autres backends, keras ou autres frameworks, scikit-learn, OpenCV, Pandas. Experience with tensorflow or other backends, keras...
From Groom & Associates - Thu, 07 Jun 2018 14:58:16 GMT - View all Montréal, QC jobs
          Platform Developer, Machine Learning - Kinaxis - Ottawa, ON      Cache   Translate Page   Web Page Cache   
Experience with Machine Learning projects, familiarity with platforms or languages such as scikit-learn, Pandas, NumPy, SciPy, R, TensorFlow....
From Kinaxis - Tue, 10 Jul 2018 14:37:13 GMT - View all Ottawa, ON jobs
          Uncle Teeth - Application Security Weekly #23      Cache   Translate Page   Web Page Cache   

This week, Keith and Paul talk The Hardest Problem in Application Security: Visibility. In the news, Google patches critical remote code execution bugs in Android OS, JavaScript API for face recognition in the browser with tensorflow.js, Social media apps are 'deliberately' addictive to users, and more on this episode of Application Security Weekly!

 

Full Show Notes: https://wiki.securityweekly.com/ASW_Episode23

 

Visit https://www.securityweekly.com/asw for all the latest episodes!

 

Visit https://www.activecountermeasures/asw to sign up for a demo or buy our AI Hunter!!

 

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          Andrejus Baranovski: Contextual Chatbot with TensorFlow, Node.js and Oracle JET - Steps How to Install and Get It Working      Cache   Translate Page   Web Page Cache   
Blog reader was asking to provide a list of steps, to guide through install and run process for chatbot solution with TensorFlow, Node.JS and Oracle JET.

Resources:

1. Chatbot UI and context handling backend implementation - Machine Learning Applied - TensorFlow Chatbot UI with Oracle JET Custom Component

read more


          How to Use MLflow, TensorFlow, and Keras with PyCharm      Cache   Translate Page   Web Page Cache   

At Spark + AI Summit in June, we announced MLflow, an open-source platform for the complete machine learning cycle. The platform’s philosophy is simple: work with any popular machine learning library; allow machine learning developers experiment with their models, preserve the training environment, parameters, and dependencies, and reproduce their results; and finally deploy, monitor and […]

The post How to Use MLflow, TensorFlow, and Keras with PyCharm appeared first on Databricks.


          ИИ может очищать фотографии от шума, обучаясь только на зашумленных фотографиях      Cache   Translate Page   Web Page Cache   

Фотография, сделанная в условиях слабого освещения, может казаться бесповоротно загубленной высоким уровнем шумов и артефактами. Однако есть способ автоматически устранять шумы и артефакты, если привлечь популярные в последнее время технологии искусственного интеллекта. Говоря точнее, алгоритмы глубокого обучения.

Этим путем пошли участники проекта, выполненного совместно специалистами Nvidia, университета Аалто и Массачусетского технологического института. Разработка была представлена на Международной конференции по машинному обучению, вчера открывшейся в Стокгольме.

В отличие от предыдущих подобных проектов, для обучения нейросети использовались не парные изображения с шумом и без, а только изображения с шумом. По словам исследователей, такой подход повышает скорость обучения, позволяя получить результаты, не уступающие обучению на парных изображениях.

Участники проекта использовали систему с графическими процессорами Nvidia Tesla P100, каркасную библиотеку глубокого обучения TensorFlow с ускорением cuDNN и 50 000 изображений из набора ImageNet.

Отметим, что удаление шумов востребовано не только в потребительской фотографии, но и при обработке МРТ-снимков в медицине. Во втором случае обучение только на зашумленных изображениях имеет решающее преимущество, поскольку невозможно получить парные МРТ-снимки без шумов.



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           不知不觉,这些似曾前沿的技术已经开始 Rebuild 我们的生活日常       Cache   Translate Page   Web Page Cache   

每一次产业变革和创业独角兽崛起,都来自成熟技术与产业的交错,每一个风口的诞生,都源自产业的横线,与向上进步的技术纵线,交汇、共振、引爆。

曾经技术缓慢积累,搜索、社交和移动互联网,每隔几年我们才看能到一个成熟的技术爆发点。今天,人工智能、自动驾驶、物联网、云计算、AR/VR······科技互联网的世界里,技术正在扎堆引爆。

AI 成为「水电煤」

越来越多的企业选择使用 Google Brain 团队开发的 TensorFlow 开源平台训练自己的 AI 产品,和 Google 一样,成熟的人工智能公司纷纷开始打造提供基础能力的服务平台。

体量较小但专注于垂直领域的公司也在寻求赋能 B 端用户的人工智能应用,推出了多个领域 AI 解决方案的小 i 机器人就是其中的佼佼者,通过其精心打造的智能交互平台,小 i 机器人已经触及到超过 8 亿的终端用户,实现了 AI 的大规模商用落地。



同样的做 AI 赋能的还有出门问问。这家以 AI 消费电子(智能手表、智能音箱、无线智能耳机)闻名的人工智能公司,于去年进行过虚拟个人助理 VPA 小问助手和免费 AI 开放平台的尝试后,又在今年推出了国内首款已量产的 AI 语音芯片模组「问芯」,为例如智能电视、机器人等更多的行业 AI 语音赋能,帮助推动消费级硬件产品的 AI 智能化迭代。



平台以外,人工智能在硬件领域的应用也有了新的进展。专注于语音智能产业的科大讯飞将多年积累的语音合成、语音识别和翻译等技术集成到一台设备里。2016 年,科大讯飞发布了「晓译翻译机」,这款可联网可离线的即时翻译设备受到了许多关注。今年四月,科大讯飞推出了讯飞翻译机 2.0,新增了摄像头和触屏功能,集成了更多语种、方言翻译、图像识别领域的 AI 引擎基础上,科大讯飞进一步拓宽了翻译机这一设备在生活中的应用。 



自动驾驶场景步步拓展

自动驾驶的热潮还在,除了 Google、百度等巨头以外,小体量公司在自动驾驶技术上的表现也不乏可圈可点之处。

景驰科技创办起,明星光环就不曾离开这家技术过硬的无人驾驶创业公司。不久前,景驰宣布从 2018 年第一季度起全年量产 500 量无人驾驶车,对一家刚刚拿下 5700 万美元 Pre-A 轮融资的初创企业而言,景驰的速度令人惊叹。



车企之外,美团点评也在积极探索无人驾驶领域。今年三月,王兴在出席活动时表示目前美团点评无人配送车已在朝阳大悦城内测试运营,预计将在 2019 年实现片区规模化运营。自动驾驶技术能够推进的不仅仅是出行行业,在一切流动的行业体系中,它都能找到落脚之地。



物联网进入家庭

物联网的概念由来已久,但普通用户切身感受到物联网技术的便利,大概要从智能音箱的风行开始说起。2017 年阿里、小米纷纷推出了低价的智能音箱,打响了中国市场的「百箱大战」。

2017 年 美国 CES 大展上,小鱼在家与百度达成战略合作,共同推出了搭载 DuerOS 的「分身鱼」视频通话机器人该款机器人是全球首个结合语音,屏幕和摄像头多模态交互体验得创新智能产品。随后 2018 年美国 CES,百度和小鱼在家再次联合推出了搭载最新的百度 DuerOS 对话式 AI 操作系统的首款带屏智能音箱「小度在家」,小鱼在家创始人兼 CEO 宋晨枫认为「小度在家」带屏智能音箱领先目前市面上的无屏智能音箱整整一代产品,是一款最适合中国家庭使用的人工智能产品。秉承「为家而生」的理念,小鱼在家发现缺失的爸爸、焦虑的妈妈、失控的孩子、守望的老人已经成为典型中国式家庭。他们还发现,目前中国用户在家庭场景下最重要的需求主要有三点,分别为:对语音交互的便利性、巨大的内容消费需求、家人的陪伴。基于此,带屏的智能音箱「小度在家」应运而生。



沉寂已久的 AR/VR 开始落地

2017 年,手机成为了 AR 技术落地的关键平台。无论是苹果还是 Google,都在其移动系统中加入了 AR 工具包,帮助开发者更便捷地开发 AR 应用和效果。

虚拟现实方面,贝壳找房的表现尤为亮眼。经过两年左右的投入和研发,贝壳找房已经可以批量生产 VR 房源信息。在贝壳找房 app 上,二手房、新房、租赁、旅居等板块均已上线 VR 看房功能,并且该应用已进入包括北京、深圳、上海在内的 40 个城市,尤其在成都市场上,贝壳平台所有新房、50% 以上的二手房已实现 VR 化。在贝壳找房的技术副总裁惠新宸看来,VR 与房地产的结合绝不只是营销噱头,这是真正有价值的应用。



云计算成为企业助推器

作为全球领先的企业应用软件解决方案提供商,SAP 可以帮助各种规模和各行业领域的企业实现更加卓越的运营。从后台到公司 管理层、从工厂仓库到商铺店面、从电脑桌面到移动终端—SAP 助力用户和企业更加高效地协作,同时更加有效地获取商业洞 见,在竞争中保持领先地位。SAP 应用和服务帮助客户实现运营盈利、不断调整和稳步增长。作为全球顶级的云供应商,SAP 拥有超过 1.56 亿云租用用户,100 多款面向所有业务线的解决方案和商务套件,在人力资本管理市场,SAP 的 SuccessFactors(HR 云)订阅用户超过 5200 万,是市场上毫无疑问的领导者。



但说起云计算就不得不提目前全球云计算服务市场份额第一的亚马逊 AWS。领跑十年的亚马逊如今正面对着来自紧随其后的微软和 Google 的竞争压力。要想保持住市场份额,更快、更好的计算能力是取胜的关键,而能够助力其计算效率成长的,就是三家主流企业都在加速研发的云端 AI。



带队亚马逊云服务研发团队的首席科学家 Animashree Anandkumar 就是那个助力 AWS 加速成长的关键人物,有幸的是,她将在两周之后来到中国成都,与数十位创新企业家一起,探讨 AI 时代云计算将如何助力企业成长。

7 月 21-22 日,成都·成华区·东郊记忆,与四十多位演讲嘉宾和上百位行业创新者一起,走近那些技术与产业的交叉点,看见决定未来的科技潮流和产业趋势。



当下,人工智能正在成为一项核心的技术,不断融入重塑再造包括零售、金融、医疗、制造、交通在内的各行各业,美团点评、京东、亚马逊等商务领域的企业早已布局人工智能技术;无人机走进了农业、工业与物流行业,开拓了更广阔的空间,大疆无人机为农业和工业的效率生产带来了更多可能;AR/VR 技术渐趋成熟,各种魔法般神奇的娱乐体验距离用户越来越近,Google 将 AR 和 VR 移植到安卓手机上,带来了许多生动有趣的内容交互体验;机器人也在重塑仓储行业,不断刷新仓储与物流的效率记录,印度机器人公司 GreyOrange 步入制造业和供应链领域,带来了全面自动化的仓储设施。

现在,你有一个机会,与亚马逊 AWS 首席科学家 Animashree Anandkumar、《区块链革命》的作者 Alex Tapscott、美团点评首席科学家夏华夏、贝壳找房副总裁、如视事业部总经理惠新宸、欢聚时代 CEO 李学凌、蔚来汽车创始人&董事长李斌和锤子科技创始人&CEO 罗永浩面对面,听 Google、Udacity、小米、京东和大疆的新想法。他们将齐聚极客公园 Rebuild 2018 大会,为我们带来他们在新技术与产业的纵横中看到的下一个创业爆发的交点。

7 月 21-22 日,在成都·成华区·东郊记忆的极客公园 Rebuild 2018 科技商业峰会上,你会找到那个答案。


题图来源:视觉中国


          tfseqestimator added to PyPI      Cache   Translate Page   Web Page Cache   
Sequence estimator for Tensorflow
          TensorFlow for Neural Network Solutions      Cache   Translate Page   Web Page Cache   
скачать TensorFlow for Neural Network Solutions бесплатно
Название: TensorFlow for Neural Network Solutions
Автор: Nick McClure
Страниц: Duration: 1h 39m
Формат: HDRip
Размер: 332,9 mb
Качество: Отличное
Язык: Английский
Жанр: Video Course
Год издания: 2018


Unleash the power of TensoeFlow to train efficient neural networks


           L'intelligenza artificiale NVidia corregge le foto affette da rumore digitale       Cache   Translate Page   Web Page Cache   
È capitato praticamente a tutti: scattando una foto in condizioni di luce scarsa l'immagine appare affetta da un evidente problema. Essa appare 'granulosa', rovinata dalla presenza di una miriade di puntini (il cosiddetto 'rumore digitale').
Nelle situazioni peggiori diventa addirittura difficoltoso riconoscere il soggetto ritratto nella foto.

I tecnici di NVidia, di concerto con i ricercatori del MIT e gli accademici della Aalto University hanno messo a punto un sistema chiamato Noise2Noise in grado di restaurare l'immagine rimuovendo ogni traccia del rumore digitale.

Per raggiungere l'ambizioso traguardo i ricercatori hanno usato una batteria di GPU NVidia Tesla P100 e il framework per il deep learning TensorFlow accelerato mediante l'utilizzo delle librerie cuDNN (CUDA Deep Neural Network), esattamente come fatto ad aprile scorso: NVidia ricostruisce le immagini danneggiate grazie all'intelligenza artificiale.


Addestrando la rete neurale con oltre 50.000 immagini provenienti dai database ImageNet (la base dati contiene sia l'immagine affetta dal rumore digitale che la versione esente da difetti), l'intelligenza artificiale così messa a punto è stata poi in grado di correggere anche le foto più problematiche.

Noise2Noise è stato in grado di riconoscere i soggetti ritratti in ciascuna immagine e di agire di conseguenza applicando le correzioni fotografiche migliori.
          ИИ может очищать фотографии от шума, обучаясь только на зашумленных фотографиях      Cache   Translate Page   Web Page Cache   

Фотография, сделанная в условиях слабого освещения, может казаться бесповоротно загубленной высоким уровнем шумов и артефактами. Однако есть способ автоматически устранять шумы и артефакты, если привлечь популярные в последнее время технологии искусственного интеллекта. Говоря точнее, алгоритмы глубокого обучения.

Искусственный интеллект научили очищать фотографии от шума

Этим путем пошли участники проекта, выполненного совместно специалистами Nvidia, университета Аалто и Массачусетского технологического института. Разработка была представлена на Международной конференции по машинному обучению, вчера открывшейся в Стокгольме.

Искусственный интеллект научили очищать фотографии от шума

В отличие от предыдущих подобных проектов, для обучения нейросети использовались не парные изображения с шумом и без, а только изображения с шумом. По словам исследователей, такой подход повышает скорость обучения, позволяя получить результаты, не уступающие обучению на парных изображениях.

Участники проекта использовали систему с графическими процессорами Nvidia Tesla P100, каркасную библиотеку глубокого обучения TensorFlow с ускорением cuDNN и 50 000 изображений из набора ImageNet.

Искусственный интеллект научили очищать фотографии от шума

Отметим, что удаление шумов востребовано не только в потребительской фотографии, но и при обработке МРТ-снимков в медицине. Во втором случае обучение только на зашумленных изображениях имеет решающее преимущество, поскольку невозможно получить парные МРТ-снимки без шумов.

Теги: Nvidia

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          Predictive Analytics with TensorFlow      Cache   Translate Page   Web Page Cache   

          Neural Network Programming with TensorFlow      Cache   Translate Page   Web Page Cache   

          Learn Data Science: My Favorite Resources      Cache   Translate Page   Web Page Cache   

When I started learning about data science, I was overwhelmed by the ocean of resources available online. Thankfully, a few practicing data scientists and professors guided me in the right direction. Below is a list of resources that I found most useful — hopefully they will kickstart your data science fascination, as they did for me.

Python

If you are completely new to programming, learning the basics of Python on Codecademy is your most-logical first step. You don’t need to be a software developer to practice data science, but you should work to become proficient at programming. As you grow your data science career, expect your programming skills to also grow.

Data Camp is a great introduction to applying Python for data science. They have many courses that will help you nail down the basics of data science. Data Camp is not free, but its pricing is approachable at $30 per month. I recommend starting with these courses:

  • Intro to Python for Data Science: Learn the Python basics, everything from variables and lists, to functions and the Python package NumPy. I recommend this course because it specifically teaches Python for doing data science.
  • Intermediate Python for Data Science: This course builds on the foundations from Intro to Python for Data Science. You will learn how to use the Python packages Matplotlib and Pandas, as well as programming fundamentals like logic, control flow, and loops.
  • Python Data Science Toolbox (Part 1 & 2): These two courses enhance your Pythonic skills. After completing these courses, you will have an understanding of functions, lambda functions, list comprehensions, and more.
  • Pandas Foundations: Pandas is one of the most widely used Python packages. This course will cover the basics of importing data, manipulating data, and conducting exploratory data analysis, all critical skills for the fledgling data scientist.
  • Statistical Thinking in Python (Part 1 & 2): If it has been a few years since your Statistics 101 course in college, or if you are completely new to statistics, you’ll find this course helpful. The course covers introductory statistical topics like summary statistics, discrete and continuous variables, confidence intervals, and hypothesis testing.

Anaconda and Jupyter Notebooks

Anaconda is an open-source data science platform. When you download Anaconda, it comes with many Python packages pre-installed, such as Pandas, NumPy, SciPy, Matplotlib, Scikit-Learn, TensorFlow, Statsmodels, NLTK, and Flask. Anaconda allows you to manage Python packages, download new packages with the Conda package manager, create Python environments, and switch between different versions of Python. Most importantly, Anaconda comes with Jupyter Notebooks. Jupyter allows you to execute Python code, review outputs from that code, and annote your data analysis using Markdown (a markup language that allows you to include narrative text). These core functionalities make Jupyter more human-interpretable than an array of Python scripts. The vast majority of my data science analyses are conducted within Jupyter Notebooks. Check out this installation guide from Quantitative Economics, if you don’t already have Anaconda and Jupyter installed.

Once you have Jupyter installed, you can learn from a wide variety of data scientists. I like to watch lectures from experts in different fields through PyData’s YouTube Channel. A lot of the PyData lecturers make their code and data publicly available on GitHub, so you can replicate their analyses on your local machine. Walking through their analysis on your own creates a great hands-on learning experience.  

For example, if you like time series analysis, check out Jeffrey Yau’s lecture on YouTube and pull his notebooks from GitHub.

SQL

SQL is another programming language many data scientists rely upon. SQL allows us to access data from relational databases. SQL Zoo is a great resource to learn the basics of SQL. Additionally, online coding platforms like Codecademy offer good SQL courses.

Deep dives into data science approaches

There are two textbooks nearly every data scientist owns, Intro to Statistical Learning (ISLR) and Elements of Statistical Learning (ESLR). Luckily, they’re both available for free in PDF version. ISLR is accessible for folks who have taken a few statistics courses in college. ESLR is intended for those who have taken more than a few statistics courses. One of my graduate school classmates spent a full two semesters combing through ESLR, asking professors and other students for help along the way.

I have also found a handful of textbooks from O’Reilly helpful and interesting:

Lastly, William Chen has a list of 22 free data science textbooks. I don’t need to recreate his list here, but definitely check out his article.

Machine Learning

Andrew Ng’s Machine Learning course at Stanford has been one of the most popular machine learning courses of all time. You can take it for free from Coursera. Machine learning is a fairly complicated subject and requires advanced knowledge of statistics and math, but if you’re up for the challenge, Ng’s course is an amazing one.

Datasets

Eventually, it will be time to leave the books and courses behind. Getting your hands on data and experimenting is a great way to learn. There is a ton of freely available data online, but I wanted to highlight a few resources I believe showcase interesting datasets.

  • Jeremy Singer Vine’s newsletter, Data is Plural, delivers new datasets weekly.
  • Kaggle is not only a great place to test out your data science chops in competitions, but is also a great data resource.
  • Fivethirtyeight publishes a fair amount of data on their GitHub account. Check out their 2018 World Cup data and analysis.

Periodical publications

Medium.com has a thriving tech and data science community. Many folks at data-science-focused firms such as AirBnB freely share new information and insights into complex problems. Towards Data Science is a curated collection of data science articles that I find highly informative — they put together a praise-worthy weekly selection.

Books for Pleasure-Reading

Nate Silver’s The Signal and the Noise, Christian Rudder’s Dataclysm, and Seth Stephens-Davidowitz’s Everybody Lies inspired me and contextualized the role of the data science in the real world.

Conclusion

You could start learning data science from almost any angle. The key is simply starting. Begin your journey with a healthy diet of statistics and programming, and see where it takes you. Maybe you will be the next Andrew Ng.


          Learn Data Science: My Favorite Resources      Cache   Translate Page   Web Page Cache   

When I started learning about data science, I was overwhelmed by the ocean of resources available online. Thankfully, a few practicing data scientists and professors guided me in the right direction. Below is a list of resources that I found most useful — hopefully they will kickstart your data science fascination, as they did for me.

Python

If you are completely new to programming, learning the basics of Python on Codecademy is your most-logical first step. You don’t need to be a software developer to practice data science, but you should work to become proficient at programming. As you grow your data science career, expect your programming skills to also grow.

Data Camp is a great introduction to applying Python for data science. They have many courses that will help you nail down the basics of data science. Data Camp is not free, but its pricing is approachable at $30 per month. I recommend starting with these courses:

  • Intro to Python for Data Science: Learn the Python basics, everything from variables and lists, to functions and the Python package NumPy. I recommend this course because it specifically teaches Python for doing data science.
  • Intermediate Python for Data Science: This course builds on the foundations from Intro to Python for Data Science. You will learn how to use the Python packages Matplotlib and Pandas, as well as programming fundamentals like logic, control flow, and loops.
  • Python Data Science Toolbox (Part 1 & 2): These two courses enhance your Pythonic skills. After completing these courses, you will have an understanding of functions, lambda functions, list comprehensions, and more.
  • Pandas Foundations: Pandas is one of the most widely used Python packages. This course will cover the basics of importing data, manipulating data, and conducting exploratory data analysis, all critical skills for the fledgling data scientist.
  • Statistical Thinking in Python (Part 1 & 2): If it has been a few years since your Statistics 101 course in college, or if you are completely new to statistics, you’ll find this course helpful. The course covers introductory statistical topics like summary statistics, discrete and continuous variables, confidence intervals, and hypothesis testing.

Anaconda and Jupyter Notebooks

Anaconda is an open-source data science platform. When you download Anaconda, it comes with many Python packages pre-installed, such as Pandas, NumPy, SciPy, Matplotlib, Scikit-Learn, TensorFlow, Statsmodels, NLTK, and Flask. Anaconda allows you to manage Python packages, download new packages with the Conda package manager, create Python environments, and switch between different versions of Python. Most importantly, Anaconda comes with Jupyter Notebooks. Jupyter allows you to execute Python code, review outputs from that code, and annote your data analysis using Markdown (a markup language that allows you to include narrative text). These core functionalities make Jupyter more human-interpretable than an array of Python scripts. The vast majority of my data science analyses are conducted within Jupyter Notebooks. Check out this installation guide from Quantitative Economics, if you don’t already have Anaconda and Jupyter installed.

Once you have Jupyter installed, you can learn from a wide variety of data scientists. I like to watch lectures from experts in different fields through PyData’s YouTube Channel. A lot of the PyData lecturers make their code and data publicly available on GitHub, so you can replicate their analyses on your local machine. Walking through their analysis on your own creates a great hands-on learning experience.  

For example, if you like time series analysis, check out Jeffrey Yau’s lecture on YouTube and pull his notebooks from GitHub.

SQL

SQL is another programming language many data scientists rely upon. SQL allows us to access data from relational databases. SQL Zoo is a great resource to learn the basics of SQL. Additionally, online coding platforms like Codecademy offer good SQL courses.

Deep dives into data science approaches

There are two textbooks nearly every data scientist owns, Intro to Statistical Learning (ISLR) and Elements of Statistical Learning (ESLR). Luckily, they’re both available for free in PDF version. ISLR is accessible for folks who have taken a few statistics courses in college. ESLR is intended for those who have taken more than a few statistics courses. One of my graduate school classmates spent a full two semesters combing through ESLR, asking professors and other students for help along the way.

I have also found a handful of textbooks from O’Reilly helpful and interesting:

Lastly, William Chen has a list of 22 free data science textbooks. I don’t need to recreate his list here, but definitely check out his article.

Machine Learning

Andrew Ng’s Machine Learning course at Stanford has been one of the most popular machine learning courses of all time. You can take it for free from Coursera. Machine learning is a fairly complicated subject and requires advanced knowledge of statistics and math, but if you’re up for the challenge, Ng’s course is an amazing one.

Datasets

Eventually, it will be time to leave the books and courses behind. Getting your hands on data and experimenting is a great way to learn. There is a ton of freely available data online, but I wanted to highlight a few resources I believe showcase interesting datasets.

  • Jeremy Singer Vine’s newsletter, Data is Plural, delivers new datasets weekly.
  • Kaggle is not only a great place to test out your data science chops in competitions, but is also a great data resource.
  • Fivethirtyeight publishes a fair amount of data on their GitHub account. Check out their 2018 World Cup data and analysis.

Periodical publications

Medium.com has a thriving tech and data science community. Many folks at data-science-focused firms such as AirBnB freely share new information and insights into complex problems. Towards Data Science is a curated collection of data science articles that I find highly informative — they put together a praise-worthy weekly selection.

Books for Pleasure-Reading

Nate Silver’s The Signal and the Noise, Christian Rudder’s Dataclysm, and Seth Stephens-Davidowitz’s Everybody Lies inspired me and contextualized the role of the data science in the real world.

Conclusion

You could start learning data science from almost any angle. The key is simply starting. Begin your journey with a healthy diet of statistics and programming, and see where it takes you. Maybe you will be the next Andrew Ng.




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