1610899440

In this video I show how the MLE algorithm works. We provide an animation where several points are classified considering three classes with mean and standard deviation values previously computed. We compute the class conditional density for the three classes and conclude the classification of a sample point with unknown class.

The source-code used to create animations and results are available at:

🔔 Subscribe: https://www.youtube.com/channel/UCSd_7rz5nzSnzUYbjaCXC5g

#machine-learning #deep-learning

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Data is everywhere. The present human lifestyle relies heavily on data. Machine learning is a huge domain that strives hard continuously to make great things out of the largely available data. With data in hand, a machine learning algorithm tries to find the pattern or the distribution of that data. Machine learning algorithms are usually defined and derived in a pattern-specific or a distribution-specific manner. For instance, Logistic Regression is a traditional machine learning algorithm meant specifically for a binary classification problem. Linear Regression is a traditional machine learning algorithm meant for the data that is linearly distributed in a multi-dimensional space. One specific algorithm cannot be applied for a problem of different nature.

To this end, Maximum Likelihood Estimation, simply known as MLE, is a traditional probabilistic approach that can be applied to data belonging to any distribution, i.e., Normal, Poisson, Bernoulli, etc. With prior assumption or knowledge about the data distribution, Maximum Likelihood Estimation helps find the most likely-to-occur distribution parameters. For instance, let us say we have data that is assumed to be normally distributed, but we do not know its mean and standard deviation parameters. Maximum Likelihood Estimation iteratively searches the most likely mean and standard deviation that could have generated the distribution. Moreover, Maximum Likelihood Estimation can be applied to both regression and classification problems.

Therefore, Maximum Likelihood Estimation is simply an optimization algorithm that searches for the most suitable parameters. Since we know the data distribution a priori, the algorithm attempts iteratively to find its pattern. The approach is much generalized, so that it is important to devise a user-defined Python function that solves the particular machine learning problem.

#developers corner #likelihood #log likelihood #maximum likelihood estimation #mle #probability distribution #python #regression #statistics

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G Suite is one of the Google products, developed form of Google Apps. It is a single platform to hold cloud computing, collaboration tools, productivity, software, and products. While using it, many a time, it’s not working, and users have a question– **How to fix G Suite not working on iPhone**? It can be resolved easily by restarting the device, and if unable to do so, you can reach our specialists whenever you want.

**For more details:** https://contactforhelp.com/blog/how-to-fix-the-g-suite-email-not-working-issue/

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Xfinity, the tradename of Comcast Cable Communications, LLC, is the first rate supplier of Internet, satellite TV, phone, and remote administrations in the United States. Presented in 2010, previously these administrations were given under the Comcast brand umbrella. Xfinity makes a universe of mind boggling amusement and innovation benefits that joins a great many individuals to the encounters and minutes that issue them the most. Since Xfinity is the greatest supplier of link administrations and home Internet in the United States, it isn’t amazing that the organization gets a ton of investigating and inquiry goal demands on its telephone based Xfinity Customer Service.

#my internet is not working comcast #comcast tv remote not working #my xfinity internet is not working #xfinity stream not working #xfinity wifi hotspot not working

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As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning models have become paramount to optimise outcomes. OpenAI’s meta-learning algorithm, Reptile, is one such model designed to perform a wide array of tasks.

For those unaware, meta-learning refers to the idea of ‘learning to learn by solving multiple tasks, like how humans learn. Using meta-learning, you can design models that can learn new skills or adapt to new environments rapidly with a few training examples.

In the recent past, the meta-learning algorithm has had a fair bit of success as it can learn with limited quantities of data. Unlike other learning models like reinforcement learning, which uses reward mechanisms for each action, meta-learning can generalise to different scenarios by separating a specified task into two functions.

The first function often gives a quick response within a specific task, while the second function includes the extraction of information learned from previous tasks. It is similar to how humans behave, where they often gain knowledge from previous unrelated tasks or experiences.

Typically, there are three common approaches to meta-learning.

- Metric-based: Learn an efficient distance metric
- Model-based: Use (recurrent) network with external or internal memory
- Optimisation-based: Optimise the model parameters explicitly for fast learning

For instance, the above image depicts the model-agnostic meta-learning algorithm (MAML) developed by researchers at the University of California, Berkeley, in partnership with OpenAI. The MAML optimises for a representation θ that can quickly adapt to new tasks.

On the other hand, Reptile utilises a stochastic gradient descent (SGD) to initialise the model’s parameters instead of performing several computations that are often resource-consuming. In other words, it also reduces the dependency of higher computational hardware requirements, if implemented in a machine learning project.

#developers corner #how reptile works #meta learning algorithm #meta-learning algorithm #algorithm

1610899440

In this video I show how the MLE algorithm works. We provide an animation where several points are classified considering three classes with mean and standard deviation values previously computed. We compute the class conditional density for the three classes and conclude the classification of a sample point with unknown class.

The source-code used to create animations and results are available at:

🔔 Subscribe: https://www.youtube.com/channel/UCSd_7rz5nzSnzUYbjaCXC5g

#machine-learning #deep-learning