VOTING POWER100.00%
DOWNVOTE POWER100.00%
RESOURCE CREDITS100.00%
REPUTATION PROGRESS0.00%
Net Worth
3.765USD
STEEM
0.000STEEM
SBD
0.000SBD
Own SP
64.909SP
Detailed Balance
| STEEM | ||
| balance | 0.000STEEM | STEEM |
| market_balance | 0.000STEEM | STEEM |
| savings_balance | 0.000STEEM | STEEM |
| reward_steem_balance | 0.000STEEM | STEEM |
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| SBD | ||
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| sbd_conversions | 0.000SBD | SBD |
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| name | ai20 |
| id | 115470 |
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| created | 2016-12-03T23:32:57 |
| recovery_account | steem |
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| next_vesting_withdrawal | 1969-12-31T23:59:59 |
| withdrawn | 0 |
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| last_account_recovery | 1970-01-01T00:00:00 |
| reset_account | null |
| last_owner_update | 1970-01-01T00:00:00 |
| last_account_update | 1970-01-01T00:00:00 |
| mined | No |
| sbd_seconds | 0 |
| sbd_last_interest_payment | 1970-01-01T00:00:00 |
| savings_sbd_last_interest_payment | 1970-01-01T00:00:00 |
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| Incoming | Outgoing |
|---|---|
Empty | Empty |
{
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}From Date
To Date
2019/12/04 00:24:18
2019/12/04 00:24:18
| author | steemitboard |
| body | Congratulations @ai20! You received a personal award! <table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@ai20/birthday3.png</td><td>Happy Birthday! - You are on the Steem blockchain for 3 years!</td></tr></table> <sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@ai20) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=ai20)_</sub> ###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes! |
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| parent permlink | 5-tips-to-learn-machine-learning-for-developers-and-beginners |
| permlink | steemitboard-notify-ai20-20191204t002417000z |
| title | |
| Transaction Info | Block #38727884/Trx 6343a5ac128355a8cc6f84a112c4e882f5daa3a8 |
View Raw JSON Data
{
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"body": "Congratulations @ai20! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@ai20/birthday3.png</td><td>Happy Birthday! - You are on the Steem blockchain for 3 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@ai20) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=ai20)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
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2018/12/04 00:36:42
| author | steemitboard |
| body | Congratulations @ai20! You received a personal award! <table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@ai20/birthday2.png</td><td>2 Years on Steemit</td></tr></table> <sub>_[Click here to view your Board of Honor](https://steemitboard.com/@ai20)_</sub> **Do not miss the last post from @steemitboard:** <table><tr><td><a href="https://steemit.com/steemitboard/@steemitboard/5jrq2c-steemitboard-saint-nicholas-day"><img src="https://steemitimages.com/64x128/http://i.cubeupload.com/mGo2Zd.png"></a></td><td><a href="https://steemit.com/steemitboard/@steemitboard/5jrq2c-steemitboard-saint-nicholas-day">Saint Nicholas challenge for good boys and girls</a></td></tr></table> > Support [SteemitBoard's project](https://steemit.com/@steemitboard)! **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**! |
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| permlink | steemitboard-notify-ai20-20181204t003641000z |
| title | |
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View Raw JSON Data
{
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"body": "Congratulations @ai20! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@ai20/birthday2.png</td><td>2 Years on Steemit</td></tr></table>\n\n<sub>_[Click here to view your Board of Honor](https://steemitboard.com/@ai20)_</sub>\n\n\n**Do not miss the last post from @steemitboard:**\n<table><tr><td><a href=\"https://steemit.com/steemitboard/@steemitboard/5jrq2c-steemitboard-saint-nicholas-day\"><img src=\"https://steemitimages.com/64x128/http://i.cubeupload.com/mGo2Zd.png\"></a></td><td><a href=\"https://steemit.com/steemitboard/@steemitboard/5jrq2c-steemitboard-saint-nicholas-day\">Saint Nicholas challenge for good boys and girls</a></td></tr></table>\n\n> Support [SteemitBoard's project](https://steemit.com/@steemitboard)! **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!",
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}smitopblockchain operation: transfer from savings2018/08/01 17:59:42
smitopblockchain operation: transfer from savings
2018/08/01 17:59:42
| amount | 0.001 SBD |
| from | smitop |
| memo | Hi, it looks like you're not voting for any witnesses. Witnesses help secure the Steem network. You should vote for some, at https://steemit.com/~witnesses, or by pressing 'Vote for witnesses' in the Steemit sidebar (top right corner). I'm a bot. |
| request id | 12831 |
| to | ai20 |
| Transaction Info | Block #24692649/Trx a8c0589a0e392f918a8d90551619f9ecb8c39c30 |
View Raw JSON Data
{
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"op": [
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}smitopblockchain operation: transfer from savings2018/08/01 17:58:00
smitopblockchain operation: transfer from savings
2018/08/01 17:58:00
| amount | 0.001 SBD |
| from | smitop |
| memo | Hi, it looks like you're not voting for any witnesses. Witnesses help secure the Steem network. You should vote for some, at https://steemit.com/~witnesses, or by pressing 'Vote for witnesses' in the Steemit sidebar (top right corner). I'm a bot. |
| request id | 12833 |
| to | ai20 |
| Transaction Info | Block #24692615/Trx e75f6cccff986c0bf05d0bd1fac4198669bd04f5 |
View Raw JSON Data
{
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"request_id": 12833,
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}2017/12/05 14:29:33
2017/12/05 14:29:33
| author | steemitboard |
| body | Congratulations @ai20! You have received a personal award! [](http://steemitboard.com/@ai20) Happy Birthday - 1 Year on Steemit Happy Birthday - 1 Year on Steemit Click on the badge to view your own Board of Honor on SteemitBoard. > By upvoting this notification, you can help all Steemit users. Learn how [here](https://steemit.com/steemitboard/@steemitboard/http-i-cubeupload-com-7ciqeo-png)! |
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| permlink | steemitboard-notify-ai20-20171205t142933000z |
| title | |
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}makaroni123upvoted (100.00%) @ai20 / 5-tips-to-learn-machine-learning-for-developers-and-beginners2017/09/01 11:26:39
makaroni123upvoted (100.00%) @ai20 / 5-tips-to-learn-machine-learning-for-developers-and-beginners
2017/09/01 11:26:39
| author | ai20 |
| permlink | 5-tips-to-learn-machine-learning-for-developers-and-beginners |
| voter | makaroni123 |
| weight | 10000 (100.00%) |
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View Raw JSON Data
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}honoluluupvoted (100.00%) @ai20 / 5-tips-to-learn-machine-learning-for-developers-and-beginners2017/09/01 11:25:03
honoluluupvoted (100.00%) @ai20 / 5-tips-to-learn-machine-learning-for-developers-and-beginners
2017/09/01 11:25:03
| author | ai20 |
| permlink | 5-tips-to-learn-machine-learning-for-developers-and-beginners |
| voter | honolulu |
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}ai20published a new post: 5-tips-to-learn-machine-learning-for-developers-and-beginners2017/09/01 11:19:33
ai20published a new post: 5-tips-to-learn-machine-learning-for-developers-and-beginners
2017/09/01 11:19:33
| author | ai20 |
| body | We live in a world in which AI, artificial intelligence, is constantly in the media to describe the future technologies that will affect our everyday life. Machine learning algorithms are most of the time at the core of an AI system and many developers and computer science practitioners want to understand this subject. Clearly, to learn machine learning algorithms and understand how to use them, many resources are available. Just to cite a few: -Intro to Machine Learning by A. Ng, video course (available at coursera http://bit.ly/1IXp8Lg and youtube) -Machine Learning by J. W. Paisley, video course (available at edX http://bit.ly/2s3pjmG) -Machine Learning A-Z™ by K. Eremenko, video course on Udemy (http://bit.ly/2s33KTx) -Machine Learning for Hackers by D. Conway and J. M. White, book R language (http://amzn.to/2t2Plv2) -Machine Learning from for the Web by A. Isoni, book Python language (http://amzn.to/2sLsgKw) -Machine Learning with Python by S. Raschka, book Python language (http://amzn.to/2rJTSi3) All the material listed above will give an excellent grasp on how to use machine learning algorithms in real scenarios and on the theory behind. I will suggest to dive into the content bearing in mind the following tips: 1. Choose a programming language and stick with it. At the moment (2017), the most used languages among data scientist professionals are R or Python. Both have great machine learning libraries, up to you. 2. Don't follow the hype, understand the basics. Machine learning is a rich field and it is tempting to jump directly to 'deep learning' or 'neuron networks'. However, you need to understand the basics to make sense of these advanced topics. 3. Focus on understanding the process not the specific algorithm. The number of details of each algorithm can be overwhelming but the essential part is to understand flow: how to go from taking a rough dataset to making predictions using different algorithms. 4. Spend 60% of the time on code, 40% on theory. Understanding the theory is important but put it in practice yourself is even more important. 5. Be aware of different prospectives. There are often different ways to see the same data and, as a consequence, the algorithms. Find different explanations for the same algorithm and try to find different interpretations for the data structure. |
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| parent author | |
| parent permlink | data |
| permlink | 5-tips-to-learn-machine-learning-for-developers-and-beginners |
| title | 5 tips to learn machine learning for developers and beginners |
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"body": "We live in a world in which AI, artificial intelligence, is constantly in the media to \ndescribe the future technologies that will affect our everyday life. Machine learning algorithms are most of the time at the core of an AI system and many developers and computer science practitioners want to understand this subject. \nClearly, to learn machine learning algorithms and understand how to use them, many resources are available.\nJust to cite a few: \n-Intro to Machine Learning by A. Ng, video course (available at coursera http://bit.ly/1IXp8Lg and youtube)\n-Machine Learning by J. W. Paisley, video course (available at edX http://bit.ly/2s3pjmG)\n-Machine Learning A-Z™ by K. Eremenko, video course on Udemy (http://bit.ly/2s33KTx)\n-Machine Learning for Hackers by D. Conway and J. M. White, book R language (http://amzn.to/2t2Plv2)\n-Machine Learning from for the Web by A. Isoni, book Python language (http://amzn.to/2sLsgKw)\n-Machine Learning with Python by S. Raschka, book Python language (http://amzn.to/2rJTSi3)\n\nAll the material listed above will give an excellent grasp on how to use machine learning algorithms in real scenarios and on the theory behind. \n\nI will suggest to dive into the content bearing in mind the following tips:\n\n1. Choose a programming language and stick with it. At the moment (2017), the most used languages among data scientist professionals are R or Python. Both have great machine learning libraries, up to you.\n\n2. Don't follow the hype, understand the basics. Machine learning is a rich field and it is tempting to jump directly to 'deep learning' or 'neuron networks'. However, you need to understand the basics to make sense of these advanced topics.\n\n3. Focus on understanding the process not the specific algorithm. The number of details of each algorithm can be overwhelming but the essential part is to understand flow: how to go from taking a rough dataset to making predictions using different algorithms.\n\n4. Spend 60% of the time on code, 40% on theory. Understanding the theory is important but put it in practice yourself is even more important.\n\n5. Be aware of different prospectives. There are often different ways to see the same data and, as a consequence, the algorithms. Find different explanations for the same algorithm and try to find different interpretations for the data structure.",
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}ai20published a new post: 5-tips-to-learn-machine-learning-for-developers-and-beginners2017/09/01 11:18:15
ai20published a new post: 5-tips-to-learn-machine-learning-for-developers-and-beginners
2017/09/01 11:18:15
| author | ai20 |
| body | We live in a world in which AI, artificial intelligence, is constantly in the media to describe the future technologies that will affect our everyday life. Machine learning algorithms are most of the time at the core of an AI system and many developers and computer science practitioners want to understand this subject. Clearly, to learn machine learning algorithms and understand how to use them, many resources are available. Just to cite a few: -Intro to Machine Learning by A. Ng, video course (available at coursera http://bit.ly/1IXp8Lg and youtube) -Machine Learning by J. W. Paisley, video course (available at edX http://bit.ly/2s3pjmG) -Machine Learning A-Z™ by K. Eremenko, video course on Udemy (http://bit.ly/2s33KTx) -Machine Learning for Hackers by D. Conway and J. M. White, book R language (http://amzn.to/2t2Plv2) -Machine Learning from for the Web by A. Isoni, book Python language (http://amzn.to/2sLsgKw) -Machine Learning with Python by S. Raschka, book Python language (http://amzn.to/2rJTSi3) All the material listed above will give an excellent grasp on how to use machine learning algorithms in real scenarios and on the theory behind. I will suggest to dive into the content bearing in mind the following tips: 1. Choose a programming language and stick with it. At the moment (2017), the most used languages among data scientist professionals are R or Python. Both have great machine learning libraries, up to you. 2. Don't follow the hype, understand the basics. Machine learning is a rich field and it is tempting to jump directly to 'deep learning' or 'neuron networks'. However, you need to understand the basics to make sense of these advanced topics. 3. Focus on understanding the process not the specific algorithm. The number of details of each algorithm can be overwhelming but the essential part is to understand flow: how to go from taking a rough dataset to making predictions using different algorithms. 4. Spend 60% of the time on code, 40% on theory. Understanding the theory is important but put it in practice yourself is even more important. 5. Be aware of different prospectives. There are often different ways to see the same data and, as a consequence, the algorithms. Find different explanations for the same algorithm and try to find different interpretations for the data structure. |
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