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Articles related to "learn"


How To Learn Java And Not Getting Bored

  • Good news: offline programming courses last much less than studying at a university.
  • For example, read a Java book you like all together with solving coding problems from the online collection.
  • Studying computer science at the university and … again, solve coding problems.
  • So my answer is this: learn Java anywhere, but solving a lot of coding problems is a must.
  • CodeGym — an online Java Core Course with 1200 coding tasks (from the easiest to pretty tough) and validator and lectures… GeeksForGeeks — a good resource with many tasks and different courses.
  • Codewars is a good site for intermediate to advanced Java students, where you solve enjoyable problems and puzzles.
  • While your coding skills grow, on the contrary, it is better to “suffer” a little longer trying to solve a tough problem.

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The 5 Classification Evaluation metrics every Data Scientist must know

  • Accuracy is a valid choice of evaluation for classification problems which are well balanced and not skewed or No class imbalance.
  • Precision is a valid choice of evaluation metric when we want to be very sure of our prediction.
  • Another very useful measure is recall, which answers a different question: what proportion of actual Positives is correctly classified?
  • Recall is a valid choice of evaluation metric when we want to capture as many positives as possible.
  • Log loss is a pretty good evaluation metric for binary classifiers and it is sometimes the optimization objective as well in case of Logistic regression and Neural Networks.
  • AUC is a good metric to use since the predictions ranked by probability is the order in which you will create a list of users to send the marketing campaign.

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Rodney Brooks – A Better Lesson

  • Just last week Rich Sutton published a very short blog post titled  The Bitter Lesson.
  • This resonates with a current mode of thinking among many of the newer entrants to AI that it is better to design learning networks and put in massive amounts of computer power, than to try to design a structure for computation that is specialized in any way for the task.
  • So my take on Rich Sutton’s piece is that the lesson we should learn from the last seventy years of AI research is not at all that we should just use more computation and that always wins.
  • Rather I think a better lesson to be learned is that we have to take into account the total cost of any solution, and that so far they have all required substantial amounts of human ingenuity.

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The 5 Classification Evaluation metrics every Data Scientist must know

  • Accuracy is a valid choice of evaluation for classification problems which are well balanced and not skewed or No class imbalance.
  • Precision is a valid choice of evaluation metric when we want to be very sure of our prediction.
  • Another very useful measure is recall, which answers a different question: what proportion of actual Positives is correctly classified?
  • Recall is a valid choice of evaluation metric when we want to capture as many positives as possible.
  • Log loss is a pretty good evaluation metric for binary classifiers and it is sometimes the optimization objective as well in case of Logistic regression and Neural Networks.
  • AUC is a good metric to use since the predictions ranked by probability is the order in which you will create a list of users to send the marketing campaign.

save | comments | report | share on


The 5 Classification Evaluation metrics every Data Scientist must know

  • Accuracy is a valid choice of evaluation for classification problems which are well balanced and not skewed or No class imbalance.
  • Precision is a valid choice of evaluation metric when we want to be very sure of our prediction.
  • Another very useful measure is recall, which answers a different question: what proportion of actual Positives is correctly classified?
  • Recall is a valid choice of evaluation metric when we want to capture as many positives as possible.
  • Log loss is a pretty good evaluation metric for binary classifiers and it is sometimes the optimization objective as well in case of Logistic regression and Neural Networks.
  • AUC is a good metric to use since the predictions ranked by probability is the order in which you will create a list of users to send the marketing campaign.

save | comments | report | share on


The 5 Classification Evaluation metrics every Data Scientist must know

  • Accuracy is a valid choice of evaluation for classification problems which are well balanced and not skewed or No class imbalance.
  • Precision is a valid choice of evaluation metric when we want to be very sure of our prediction.
  • Another very useful measure is recall, which answers a different question: what proportion of actual Positives is correctly classified?
  • Recall is a valid choice of evaluation metric when we want to capture as many positives as possible.
  • Log loss is a pretty good evaluation metric for binary classifiers and it is sometimes the optimization objective as well in case of Logistic regression and Neural Networks.
  • AUC is a good metric to use since the predictions ranked by probability is the order in which you will create a list of users to send the marketing campaign.

save | comments | report | share on


How To Learn Java And Not Getting Bored

  • Good news: offline programming courses last much less than studying at a university.
  • For example, read a Java book you like all together with solving coding problems from the online collection.
  • Studying computer science at the university and … again, solve coding problems.
  • So my answer is this: learn Java anywhere, but solving a lot of coding problems is a must.
  • CodeGym — an online Java Core Course with 1200 coding tasks (from the easiest to pretty tough) and validator and lectures… GeeksForGeeks — a good resource with many tasks and different courses.
  • Codewars is a good site for intermediate to advanced Java students, where you solve enjoyable problems and puzzles.
  • While your coding skills grow, on the contrary, it is better to “suffer” a little longer trying to solve a tough problem.

save | comments | report | share on


OpenAI teaches AI teamwork by playing hide-and-seek

  • As with this research, the AI agents weren’t taught the rules of the game beforehand, yet they learned basic strategies over time and eventually surpassed most human players in skill.
  • (“Line of sight” in this context refers to 135-degree cones in front of individual agents.) Agents were penalized if they ventured too far outside the play area and were forced to navigate randomly generated rooms and walls, and they could pick up objects (mainly boxes) scattered throughout the environment that locked into place indefinitely.
  • At first, hiders and seekers merely ran away and chased each other, but after roughly 25 million matches of hide-and-seek, the hiders learned to construct concealing shelters by moving boxes together and against walls.
  • On three out of five of the tasks, the agents pretrained in the hide-and-seek environment learned faster and achieved a higher final reward than both baselines.

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Emergent Tool Use from Multi-Agent Interaction

  • Note that there are no direct incentives for agents to interact with objects or to explore; rather, the emergent strategies shown below are a result of the autocurriculum induced by multi-agent competition and the simple dynamics of hide-and-seek.
  • Another method to learn skills in an unsupervised manner is intrinsic motivation, which incentivizes agents to explore with various metrics such as model error or state counts.
  • For this reason, we believe multi-agent competition will be a more scalable method for generating human-relevant skills in an unsupervised manner as environments continue to increase in size and complexity.
  • We've provided evidence that human-relevant strategies and skills, far more complex than the seed game dynamics and environment, can emerge from multi-agent competition and standard reinforcement learning algorithms at scale.

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The First Step After Deciding To Become A Developer And Get Your First Job

  • This post is specifically for anyone who has made their mind to focus on learning programming and sharpen their coding skills to get the first job in tech.
  • So fastforward I decided to quit my dental career and focus on getting into tech.
  • I didn't want to invest money in learning a new skill by going to college for the next couple of years.
  • When I started my journey, I chose to go through the self-taught route, and let me be clear that it isn't easy.
  • But nobody is going to ask after how many days you learned certain technology to build a particular project.
  • Even after getting a job, you need to learn every day because there's just so much to learn.
  • Practice, persevere and join the community of people who're following this route, you'll feel less lonely and more optimistic.

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