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      "author": "tai-euler",
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      "title": "The finest resources for Machine Learning & Deep Learning (coursera, fast.ai, kaggle and many more )",
      "body": "https://balkanandroid.com/wp-content/uploads/2018/02/deepfakes-fakeapp-640x399.jpg.pagespeed.ce.1dHYNg4Szq.jpg\n\nIts 2018 and if you want to get in on this right?!\nYour first step is to study AI on basic level to bring some of your own nasty AI solutions.\n\n# 1. Free online videos\n\nSome excellent playlists to watch (ordered for better understanding):\n\n<center>Learn Python for Data Science</center>\nhttps://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU\n<hr>\n<hr>\n<center>Intro to Tensorflow</center>\nhttps://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV\n<hr>\n<hr>\n<center>Intro to Deep Learning (Udacity Nanodegree) </center>\nhttps://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3\n<hr>\n<hr>\n<center>The Math of Intelligence </center>\nhttps://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D\n<hr>\n<hr>\n\n# 2. Online courses\n\nSecond you have to suck in knowledge from online courses, some excellent resources are:\n\n\n### <center> Andrew Ng - Deep Learning Specialization</center>\n<center>https://www.coursera.org/specializations/deep-learning</center>\n<center>About Andrew Ng: Co-founder, Coursera;  Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain and really an \"AI rockstar\" in the AI community\n</center>\n![Screen Shot 2018-02-26 at 16.40.33.png](https://steemitimages.com/DQmaPGS14a5csq5ndmVfHkWkhWhfwz3h9VpGz5qhGkN7qrc/Screen%20Shot%202018-02-26%20at%2016.40.33.png)\n<hr>\n<hr>\n\n### <center>Jeremy Howard - fast.ai</center>\n<center> http://course.fast.ai/ </center>\n\n![Screen Shot 2018-02-26 at 16.41.31.png](https://steemitimages.com/DQmNrVD9ko1qu3Cz579WC3xwDVmF8jXDHvEN8U44o8iESXj/Screen%20Shot%202018-02-26%20at%2016.41.31.png)\n<hr>\n<hr>\n\n# 3. Books\n\nThird, read some books! One great choice would be this one:\n### <center> Ian Goodfellow and Yoshua Bengio and Aaron Courville - Deep Learning</center>\n<center> It helps you to with the terms in math of deep learning.\nAvailable for free on http://www.deeplearningbook.org/ </center>\n<hr>\n<hr>\n\n# 4. Collect your Data\n\nEver heard about \"garbage in, garbage out\"?\nThe quality of your data is the most important part of the machine learning pipeline even more so than the architecture of your AI model. \n\n### <center>Github </center>\n<center>The easy way to get data is to search for public datasets. You found awesome datasets on Github.\nhttps://github.com/awesomedata/awesome-public-datasets</center>\n\n![Screen Shot 2018-02-26 at 17.15.24.png](https://steemitimages.com/DQmVTnuSt6mqE8MdPRDk5iXA6mDEajv7CYB2U2VoRw15qNg/Screen%20Shot%202018-02-26%20at%2017.15.24.png)\n<hr>\n<hr>\n\n### <center>UC Irvine Machine Learning Repository</center>\n<center> https://archive.ics.uci.edu/ml/index.php </center>\n\n![Screen Shot 2018-02-26 at 17.16.25.png](https://steemitimages.com/DQmRu3qXo2xuKW4MmnrR7HxxcwgsP3pbbnCtvCEJYiR2MU9/Screen%20Shot%202018-02-26%20at%2017.16.25.png)\n<hr>\n<hr>\n\n### <center> Kaggle Datasets</center>\n<center> https://www.kaggle.com/datasets </center>\n\n![Screen Shot 2018-02-26 at 17.18.00.png](https://steemitimages.com/DQmW7i2gPmbqDqZV2Ri8h55mCScDvdQcXDGL9XMdJQXLEn3/Screen%20Shot%202018-02-26%20at%2017.18.00.png)\n<hr>\n<hr>\n\n### Create/buy your own datasets\n\n\n<center> DataCircle - where you can buy or directly exchange datasets with other people \nhttps://datacircle.io/ </center>\n\n![Screen Shot 2018-02-26 at 17.23.27.png](https://steemitimages.com/DQmbaTQFc2iTb25LNZ3sD9WFRa9skiMJx2texevL36BrCfv/Screen%20Shot%202018-02-26%20at%2017.23.27.png)\n <hr>\n<hr>\n\n<center> Scrape data yourself using python with a library like Scrapy, Selenium, Beautiful Soup 4 etc.\nExample: Retrieve Images from Wikipedia - https://gist.github.com/iwek/3100809  </center>\n![Screen Shot 2018-02-26 at 17.29.58.png](https://steemitimages.com/DQmS2vWzRydB4a23bTTBhQC9myjxckoJAyPndbLwBEHmFoA/Screen%20Shot%202018-02-26%20at%2017.29.58.png)\n\n\n<center> Diggernaut - turn website content into datasets. No programming skills required\nhttps://www.diggernaut.com/ </center>\n![Screen Shot 2018-02-26 at 17.31.19.png](https://steemitimages.com/DQmYpQQcbouPgcMjUPfXZZZ5xpY8pRGrp1oLn1mmoEAhj52/Screen%20Shot%202018-02-26%20at%2017.31.19.png)\n<hr>\n<hr>\n\n### Last words\n\nKeep in mind, big companies like Google have a huge advantage when it comes to building horizontal products that can apply to many industries like image recognition, language translation or infrastructure. But the advantage that smaller players have, like you, is that you can move fast on a single problem vertically. The big ones dont have to time to tackle every single niche problem. But you do! You can focus on the enterprise and build some niche solution that would help companies (and sell it to them). \n\nOne way to raise awareness of your product is to raising your own personal profile, establish yourself as an AI thought leader and publish blog posts on https://medium.com . Create blog content that answers fundamental questions about AI and build an audience. Then share it on Social Media or on Hacker News (https://news.ycombinator.com).\nExample https://medium.com/@dtfoster\n<hr>\n<hr>\n\n![Screen Shot 2018-02-26 at 17.13.08.png](https://steemitimages.com/DQmRT5ATB59QJHyoLe1tYnhw14RGC63B2hz9T9EMJtKKsKP/Screen%20Shot%202018-02-26%20at%2017.13.08.png)\n<hr>\n<hr>\n\nLet me end this with an inspirational meme :\n\n![Screen Shot 2018-02-26 at 17.11.06.png](https://steemitimages.com/DQmVPgoDWW6ayBcceTSgzJpdnvkufJkKeaezpf9JstJQ5KH/Screen%20Shot%202018-02-26%20at%2017.11.06.png)\n<hr>\n<hr>\n\n### <center>  If you enjoyed this article please Upvote and Resteem :) </center>\n<hr>\n<hr>",
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      "title": "The finest resources for Machine Learning & Deep Learning",
      "body": "https://yowbuzz.com/wp-content/uploads/2018/02/Deep1_face-720x405.jpg\n\nIts 2018 and if you want to get in on this right?!\nYour first step is to study AI on basic level to bring some of your own nasty AI solutions.\n\n# 1. Free online videos\n\nSome excellent playlists to watch (ordered for better understanding):\n\n<center>Learn Python for Data Science</center>\nhttps://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU\n<hr>\n<hr>\n<center>Intro to Tensorflow</center>\nhttps://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV\n<hr>\n<hr>\n<center>Intro to Deep Learning (Udacity Nanodegree) </center>\nhttps://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3\n<hr>\n<hr>\n<center>The Math of Intelligence </center>\nhttps://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D\n<hr>\n<hr>\n\n# 2. Online courses\n\nSecond you have to suck in knowledge from online courses, some excellent resources are:\n\n\n### <center> Andrew Ng - Deep Learning Specialization</center>\n<center>https://www.coursera.org/specializations/deep-learning</center>\n<center>About Andrew Ng: Co-founder, Coursera;  Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain and really an \"AI rockstar\" in the AI community\n</center>\n![Screen Shot 2018-02-26 at 16.40.33.png](https://steemitimages.com/DQmaPGS14a5csq5ndmVfHkWkhWhfwz3h9VpGz5qhGkN7qrc/Screen%20Shot%202018-02-26%20at%2016.40.33.png)\n<hr>\n<hr>\n\n### <center>Jeremy Howard - fast.ai</center>\n<center> http://course.fast.ai/ </center>\n\n![Screen Shot 2018-02-26 at 16.41.31.png](https://steemitimages.com/DQmNrVD9ko1qu3Cz579WC3xwDVmF8jXDHvEN8U44o8iESXj/Screen%20Shot%202018-02-26%20at%2016.41.31.png)\n<hr>\n<hr>\n\n# 3. Books\n\nThird, read some books! One great choice would be this one:\n### <center> Ian Goodfellow and Yoshua Bengio and Aaron Courville - Deep Learning</center>\n<center> It helps you to with the terms in math of deep learning.\nAvailable for free on http://www.deeplearningbook.org/ </center>\n<hr>\n<hr>\n\n# 4. Collect your Data\n\nEver heard about \"garbage in, garbage out\"?\nThe quality of your data is the most important part of the machine learning pipeline even more so than the architecture of your AI model. \n\n### <center>Github </center>\n<center>The easy way to get data is to search for public datasets. You found awesome datasets on Github.\nhttps://github.com/awesomedata/awesome-public-datasets</center>\n\n![Screen Shot 2018-02-26 at 17.15.24.png](https://steemitimages.com/DQmVTnuSt6mqE8MdPRDk5iXA6mDEajv7CYB2U2VoRw15qNg/Screen%20Shot%202018-02-26%20at%2017.15.24.png)\n<hr>\n<hr>\n\n### <center>UC Irvine Machine Learning Repository</center>\n<center> https://archive.ics.uci.edu/ml/index.php </center>\n\n![Screen Shot 2018-02-26 at 17.16.25.png](https://steemitimages.com/DQmRu3qXo2xuKW4MmnrR7HxxcwgsP3pbbnCtvCEJYiR2MU9/Screen%20Shot%202018-02-26%20at%2017.16.25.png)\n<hr>\n<hr>\n\n### <center> Kaggle Datasets</center>\n<center> https://www.kaggle.com/datasets </center>\n\n![Screen Shot 2018-02-26 at 17.18.00.png](https://steemitimages.com/DQmW7i2gPmbqDqZV2Ri8h55mCScDvdQcXDGL9XMdJQXLEn3/Screen%20Shot%202018-02-26%20at%2017.18.00.png)\n<hr>\n<hr>\n\n### Create/buy your own datasets\n\n\n<center> DataCircle - where you can buy or directly exchange datasets with other people \nhttps://datacircle.io/ </center>\n\n![Screen Shot 2018-02-26 at 17.23.27.png](https://steemitimages.com/DQmbaTQFc2iTb25LNZ3sD9WFRa9skiMJx2texevL36BrCfv/Screen%20Shot%202018-02-26%20at%2017.23.27.png)\n <hr>\n<hr>\n\n<center> Scrape data yourself using python with a library like Scrapy, Selenium, Beautiful Soup 4 etc.\nExample: Retrieve Images from Wikipedia - https://gist.github.com/iwek/3100809  </center>\n![Screen Shot 2018-02-26 at 17.29.58.png](https://steemitimages.com/DQmS2vWzRydB4a23bTTBhQC9myjxckoJAyPndbLwBEHmFoA/Screen%20Shot%202018-02-26%20at%2017.29.58.png)\n\n\n<center> Diggernaut - turn website content into datasets. No programming skills required\nhttps://www.diggernaut.com/ </center>\n![Screen Shot 2018-02-26 at 17.31.19.png](https://steemitimages.com/DQmYpQQcbouPgcMjUPfXZZZ5xpY8pRGrp1oLn1mmoEAhj52/Screen%20Shot%202018-02-26%20at%2017.31.19.png)\n<hr>\n<hr>\n\n### Last words\n\nKeep in mind, big companies like Google have a huge advantage when it comes to building horizontal products that can apply to many industries like image recognition, language translation or infrastructure. But the advantage that smaller players have, like you, is that you can move fast on a single problem vertically. The big ones dont have to time to tackle every single niche problem. But you do! You can focus on the enterprise and build some niche solution that would help companies (and sell it to them). \n\nOne way to raise awareness of your product is to raising your own personal profile, establish yourself as an AI thought leader and publish blog posts on https://medium.com . Create blog content that answers fundamental questions about AI and build an audience. Then share it on Social Media or on Hacker News (https://news.ycombinator.com).\nExample https://medium.com/@dtfoster\n<hr>\n<hr>\n\n![Screen Shot 2018-02-26 at 17.13.08.png](https://steemitimages.com/DQmRT5ATB59QJHyoLe1tYnhw14RGC63B2hz9T9EMJtKKsKP/Screen%20Shot%202018-02-26%20at%2017.13.08.png)\n<hr>\n<hr>\n\nLet me end this with an inspirational meme :\n\n![Screen Shot 2018-02-26 at 17.11.06.png](https://steemitimages.com/DQmVPgoDWW6ayBcceTSgzJpdnvkufJkKeaezpf9JstJQ5KH/Screen%20Shot%202018-02-26%20at%2017.11.06.png)\n<hr>\n<hr>\n\n### <center>  If you enjoyed this article please Upvote and Resteem :) </center>\n![Screen Shot 2018-02-26 at 17.08.22.png](https://steemitimages.com/DQmUWynVvcXueoxGGDqZmnW5mEGfStgzhH8dpyYb27pWqjk/Screen%20Shot%202018-02-26%20at%2017.08.22.png)\n\n#steemit #programming #deeplearning #deepfake",
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      "title": "Machine Learning | Deep Learning (finest resources)",
      "body": "https://yowbuzz.com/wp-content/uploads/2018/02/Deep1_face-720x405.jpg\n\nIts 2018 and if you want to get in on this right?!\nYour first step is to study AI on basic level to bring some of your own nasty AI solutions.\n\n# 1. Free online videos\n\nSome excellent playlists to watch (ordered for better understanding):\n\n<center>Learn Python for Data Science</center>\nhttps://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU\n<hr>\n<hr>\n<center>Intro to Tensorflow</center>\nhttps://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV\n<hr>\n<hr>\n<center>Intro to Deep Learning (Udacity Nanodegree) </center>\nhttps://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3\n<hr>\n<hr>\n<center>The Math of Intelligence </center>\nhttps://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D\n<hr>\n<hr>\n\n# 2. Online courses\n\nSecond you have to suck in knowledge from online courses, some excellent resources are:\n\n\n### <center> Andrew Ng - Deep Learning Specialization</center>\n<center>https://www.coursera.org/specializations/deep-learning</center>\n<center>About Andrew Ng: Co-founder, Coursera;  Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain and really an \"AI rockstar\" in the AI community\n</center>\n![Screen Shot 2018-02-26 at 16.40.33.png](https://steemitimages.com/DQmaPGS14a5csq5ndmVfHkWkhWhfwz3h9VpGz5qhGkN7qrc/Screen%20Shot%202018-02-26%20at%2016.40.33.png)\n<hr>\n<hr>\n\n### <center>Jeremy Howard - fast.ai</center>\n<center> http://course.fast.ai/ </center>\n\n![Screen Shot 2018-02-26 at 16.41.31.png](https://steemitimages.com/DQmNrVD9ko1qu3Cz579WC3xwDVmF8jXDHvEN8U44o8iESXj/Screen%20Shot%202018-02-26%20at%2016.41.31.png)\n<hr>\n<hr>\n\n# 3. Books\n\nThird, read some books! One great choice would be this one:\n### <center> Ian Goodfellow and Yoshua Bengio and Aaron Courville - Deep Learning</center>\n<center> It helps you to with the terms in math of deep learning.\nAvailable for free on http://www.deeplearningbook.org/ </center>\n<hr>\n<hr>\n\n# 4. Collect your Data\n\nEver heard about \"garbage in, garbage out\"?\nThe quality of your data is the most important part of the machine learning pipeline even more so than the architecture of your AI model. \n\n### <center>Github </center>\n<center>The easy way to get data is to search for public datasets. You found awesome datasets on Github.\nhttps://github.com/awesomedata/awesome-public-datasets</center>\n\n![Screen Shot 2018-02-26 at 17.15.24.png](https://steemitimages.com/DQmVTnuSt6mqE8MdPRDk5iXA6mDEajv7CYB2U2VoRw15qNg/Screen%20Shot%202018-02-26%20at%2017.15.24.png)\n<hr>\n<hr>\n\n### <center>UC Irvine Machine Learning Repository</center>\n<center> https://archive.ics.uci.edu/ml/index.php </center>\n\n![Screen Shot 2018-02-26 at 17.16.25.png](https://steemitimages.com/DQmRu3qXo2xuKW4MmnrR7HxxcwgsP3pbbnCtvCEJYiR2MU9/Screen%20Shot%202018-02-26%20at%2017.16.25.png)\n<hr>\n<hr>\n\n### <center> Kaggle Datasets</center>\n<center> https://www.kaggle.com/datasets </center>\n\n![Screen Shot 2018-02-26 at 17.18.00.png](https://steemitimages.com/DQmW7i2gPmbqDqZV2Ri8h55mCScDvdQcXDGL9XMdJQXLEn3/Screen%20Shot%202018-02-26%20at%2017.18.00.png)\n<hr>\n<hr>\n\n### Create/buy your own datasets\n\n\n<center> DataCircle - where you can buy or directly exchange datasets with other people \nhttps://datacircle.io/ </center>\n\n![Screen Shot 2018-02-26 at 17.23.27.png](https://steemitimages.com/DQmbaTQFc2iTb25LNZ3sD9WFRa9skiMJx2texevL36BrCfv/Screen%20Shot%202018-02-26%20at%2017.23.27.png)\n <hr>\n<hr>\n\n<center> Scrape data yourself using python with a library like Scrapy, Selenium, Beautiful Soup 4 etc.\nExample: Retrieve Images from Wikipedia - https://gist.github.com/iwek/3100809  </center>\n![Screen Shot 2018-02-26 at 17.29.58.png](https://steemitimages.com/DQmS2vWzRydB4a23bTTBhQC9myjxckoJAyPndbLwBEHmFoA/Screen%20Shot%202018-02-26%20at%2017.29.58.png)\n\n\n<center> Diggernaut - turn website content into datasets. No programming skills required\nhttps://www.diggernaut.com/ </center>\n![Screen Shot 2018-02-26 at 17.31.19.png](https://steemitimages.com/DQmYpQQcbouPgcMjUPfXZZZ5xpY8pRGrp1oLn1mmoEAhj52/Screen%20Shot%202018-02-26%20at%2017.31.19.png)\n<hr>\n<hr>\n\n### Last words\n\nKeep in mind, big companies like Google have a huge advantage when it comes to building horizontal products that can apply to many industries like image recognition, language translation or infrastructure. But the advantage that smaller players have, like you, is that you can move fast on a single problem vertically. The big ones dont have to time to tackle every single niche problem. But you do! You can focus on the enterprise and build some niche solution that would help companies (and sell it to them). \n\nOne way to raise awareness of your product is to raising your own personal profile, establish yourself as an AI thought leader and publish blog posts on https://medium.com . Create blog content that answers fundamental questions about AI and build an audience. Then share it on Social Media or on Hacker News (https://news.ycombinator.com).\nExample https://medium.com/@dtfoster\n<hr>\n<hr>\n\n![Screen Shot 2018-02-26 at 17.13.08.png](https://steemitimages.com/DQmRT5ATB59QJHyoLe1tYnhw14RGC63B2hz9T9EMJtKKsKP/Screen%20Shot%202018-02-26%20at%2017.13.08.png)\n<hr>\n<hr>\n\nLet me end this with an inspirational meme :\n\n![Screen Shot 2018-02-26 at 17.11.06.png](https://steemitimages.com/DQmVPgoDWW6ayBcceTSgzJpdnvkufJkKeaezpf9JstJQ5KH/Screen%20Shot%202018-02-26%20at%2017.11.06.png)\n<hr>\n<hr>\n\n### <center>  If you enjoyed this article please Upvote and Resteem :) </center>\n![Screen Shot 2018-02-26 at 17.08.22.png](https://steemitimages.com/DQmUWynVvcXueoxGGDqZmnW5mEGfStgzhH8dpyYb27pWqjk/Screen%20Shot%202018-02-26%20at%2017.08.22.png)\n\n#steem #programming #deeplearning #deepfake",
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      "author": "tai-euler",
      "permlink": "machine-learning-or-deep-learning-finest-resources",
      "title": "Machine Learning | Deep Learning (finest resources)",
      "body": "https://i2.wp.com/www.thedeepfakesociety.com/wp-content/uploads/2018/02/wonder-woman-deepfakes-deep-fakes.jpg?fit=480%2C360&ssl=1&w=640\n\nIts 2018 and if you want to get in on this right?!\nYour first step is to study AI on basic level to bring some of your own nasty AI solutions.\n\n# 1. Free online videos\n\nSome excellent playlists to watch (ordered for better understanding):\n\n<center>Learn Python for Data Science</center>\nhttps://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU\n<hr>\n<hr>\n<center>Intro to Tensorflow</center>\nhttps://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV\n<hr>\n<hr>\n<center>Intro to Deep Learning (Udacity Nanodegree) </center>\nhttps://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3\n<hr>\n<hr>\n<center>The Math of Intelligence </center>\nhttps://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D\n<hr>\n<hr>\n\n# 2. Online courses\n\nSecond you have to suck in knowledge from online courses, some excellent resources are:\n\n\n### <center> Andrew Ng - Deep Learning Specialization</center>\n<center>https://www.coursera.org/specializations/deep-learning</center>\n<center>About Andrew Ng: Co-founder, Coursera;  Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain and really an \"AI rockstar\" in the AI community\n</center>\n![Screen Shot 2018-02-26 at 16.40.33.png](https://steemitimages.com/DQmaPGS14a5csq5ndmVfHkWkhWhfwz3h9VpGz5qhGkN7qrc/Screen%20Shot%202018-02-26%20at%2016.40.33.png)\n<hr>\n<hr>\n\n### <center>Jeremy Howard - fast.ai</center>\n<center> http://course.fast.ai/ </center>\n\n![Screen Shot 2018-02-26 at 16.41.31.png](https://steemitimages.com/DQmNrVD9ko1qu3Cz579WC3xwDVmF8jXDHvEN8U44o8iESXj/Screen%20Shot%202018-02-26%20at%2016.41.31.png)\n<hr>\n<hr>\n\n# 3. Books\n\nThird, read some books! One great choice would be this one:\n### <center> Ian Goodfellow and Yoshua Bengio and Aaron Courville - Deep Learning</center>\n<center> It helps you to with the terms in math of deep learning.\nAvailable for free on http://www.deeplearningbook.org/ </center>\n<hr>\n<hr>\n\n# 4. Collect your Data\n\nEver heard about \"garbage in, garbage out\"?\nThe quality of your data is the most important part of the machine learning pipeline even more so than the architecture of your AI model. \n\n### <center>Github </center>\n<center>The easy way to get data is to search for public datasets. You found awesome datasets on Github.\nhttps://github.com/awesomedata/awesome-public-datasets</center>\n\n![Screen Shot 2018-02-26 at 17.15.24.png](https://steemitimages.com/DQmVTnuSt6mqE8MdPRDk5iXA6mDEajv7CYB2U2VoRw15qNg/Screen%20Shot%202018-02-26%20at%2017.15.24.png)\n<hr>\n<hr>\n\n### <center>UC Irvine Machine Learning Repository</center>\n<center> https://archive.ics.uci.edu/ml/index.php </center>\n\n![Screen Shot 2018-02-26 at 17.16.25.png](https://steemitimages.com/DQmRu3qXo2xuKW4MmnrR7HxxcwgsP3pbbnCtvCEJYiR2MU9/Screen%20Shot%202018-02-26%20at%2017.16.25.png)\n<hr>\n<hr>\n\n### <center> Kaggle Datasets</center>\n<center> https://www.kaggle.com/datasets </center>\n\n![Screen Shot 2018-02-26 at 17.18.00.png](https://steemitimages.com/DQmW7i2gPmbqDqZV2Ri8h55mCScDvdQcXDGL9XMdJQXLEn3/Screen%20Shot%202018-02-26%20at%2017.18.00.png)\n<hr>\n<hr>\n\n### Create/buy your own datasets\n\n\n<center> DataCircle - where you can buy or directly exchange datasets with other people \nhttps://datacircle.io/ </center>\n\n![Screen Shot 2018-02-26 at 17.23.27.png](https://steemitimages.com/DQmbaTQFc2iTb25LNZ3sD9WFRa9skiMJx2texevL36BrCfv/Screen%20Shot%202018-02-26%20at%2017.23.27.png)\n <hr>\n<hr>\n\n<center> Scrape data yourself using python with a library like Scrapy, Selenium, Beautiful Soup 4 etc.\nExample: Retrieve Images from Wikipedia - https://gist.github.com/iwek/3100809  </center>\n![Screen Shot 2018-02-26 at 17.29.58.png](https://steemitimages.com/DQmS2vWzRydB4a23bTTBhQC9myjxckoJAyPndbLwBEHmFoA/Screen%20Shot%202018-02-26%20at%2017.29.58.png)\n\n\n<center> Diggernaut - turn website content into datasets. No programming skills required\nhttps://www.diggernaut.com/ </center>\n![Screen Shot 2018-02-26 at 17.31.19.png](https://steemitimages.com/DQmYpQQcbouPgcMjUPfXZZZ5xpY8pRGrp1oLn1mmoEAhj52/Screen%20Shot%202018-02-26%20at%2017.31.19.png)\n<hr>\n<hr>\n\n### Last words\n\nKeep in mind, big companies like Google have a huge advantage when it comes to building horizontal products that can apply to many industries like image recognition, language translation or infrastructure. But the advantage that smaller players have, like you, is that you can move fast on a single problem vertically. The big ones dont have to time to tackle every single niche problem. But you do! You can focus on the enterprise and build some niche solution that would help companies (and sell it to them). \n\nOne way to raise awareness of your product is to raising your own personal profile, establish yourself as an AI thought leader and publish blog posts on https://medium.com . Create blog content that answers fundamental questions about AI and build an audience. Then share it on Social Media or on Hacker News (https://news.ycombinator.com).\nExample https://medium.com/@dtfoster\n<hr>\n<hr>\n\n![Screen Shot 2018-02-26 at 17.13.08.png](https://steemitimages.com/DQmRT5ATB59QJHyoLe1tYnhw14RGC63B2hz9T9EMJtKKsKP/Screen%20Shot%202018-02-26%20at%2017.13.08.png)\n<hr>\n<hr>\n\nLet me end this with an inspirational meme :\n\n![Screen Shot 2018-02-26 at 17.11.06.png](https://steemitimages.com/DQmVPgoDWW6ayBcceTSgzJpdnvkufJkKeaezpf9JstJQ5KH/Screen%20Shot%202018-02-26%20at%2017.11.06.png)\n<hr>\n<hr>\n\n### <center>  If you enjoyed this article please Upvote and Resteem :) </center>\n![Screen Shot 2018-02-26 at 17.08.22.png](https://steemitimages.com/DQmUWynVvcXueoxGGDqZmnW5mEGfStgzhH8dpyYb27pWqjk/Screen%20Shot%202018-02-26%20at%2017.08.22.png)",
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