Sign Up Now!

Sign up and get personalized intelligence briefing delivered daily.


Sign Up

Articles related to "learn"


Regularization — Part 3

  • At the input of the layer, you start measuring the mean and the standard deviation of the batch.
  • So what you do is you compute over the mini-batch, the current mean and standard deviation, and then you use that to normalize the activations of the input.
  • If you want to move on towards test time, you compute — after you finish the training — the mean and the standard deviation in the batch normalization layer once for the entire training set and you keep it and constant for all future applications of the network.
  • But if you want to have independent features that allow you to recognize different things, then you somehow have to break the correlation between the features and this can actually be performed by dropout.

save | comments | report | share on


Model with TensorFlow and Serve on Google Cloud Platform

  • In this guide, we learn how to develop a TensorFlow model and serve it on the Google Cloud Platform (GCP).
  • We consider a regression problem of predicting the earnings of products using a three-layer neural network implemented with TensorFlow and Keras APIs. In this guide, we will use the TensorFlow 2.1.0 and Google colab runtime environment.
  • Google colab offers the training of machine learning models on free GPU and TPU.
  • To define the deep learning model, we use the Keras API module shipped with TensorFlow 2.1.0 We start with a basic model with two intermediate relu layers.
  • Let us read the sample input data and test our model to predict and save the model locally for future use.
  • In this guide, we have learned about training the deep learning models with TensorFlow 2.1.0 and deploying the trained model on the google cloud platform.

save | comments | report | share on


My 10 favorite resources for learning data science online

  • That is why in this article, I want to share my 10 favorite data science resources (online ones), which I frequently use for learning and trying to keep up with the current developments.
  • In the talks you can find a mix of general Python best practices, examples of real-life cases the data scientists worked on (for example, how they model churn or what tools they use to generate an uplift in their marketing campaigns), and introductions to some new libraries.
  • I started my data science journey with R, and even after switching my main programming language to Python I still follow R-bloggers.
  • The list of people to follow will highly depend on the scope of your interests, for example, if you focus on deep learning used for computer vision or maybe NLP.

save | comments | report | share on


Regularization — Part 3

  • At the input of the layer, you start measuring the mean and the standard deviation of the batch.
  • So what you do is you compute over the mini-batch, the current mean and standard deviation, and then you use that to normalize the activations of the input.
  • If you want to move on towards test time, you compute — after you finish the training — the mean and the standard deviation in the batch normalization layer once for the entire training set and you keep it and constant for all future applications of the network.
  • But if you want to have independent features that allow you to recognize different things, then you somehow have to break the correlation between the features and this can actually be performed by dropout.

save | comments | report | share on


Basic Linear Regression Algorithm in Python for Beginners

  • The most basic machine learning algorithm has to be the linear regression algorithm with a single variable.
  • Nowadays, there are so many advanced machine learning algorithms, libraries, and techniques available that linear regression may seem to be not important.
  • In this article, I will explain the linear regression algorithm step by step.
  • Here, ‘h’ is the hypothesis or the predicted dependent variable, X is the input feature, and theta0 and theta1 are the coefficients.
  • Then using gradient descent, we will update the theta value to minimize the cost function.
  • In this dataset, column zero is the input feature and column 1 is the output variable or dependent variable.
  • As I mentioned before, our purpose was to optimize the theta values to minimize the cost.
  • That means the theta values are optimized correctly as we expected.

save | comments | report | share on


Basic Linear Regression Algorithm in Python for Beginners

  • The most basic machine learning algorithm has to be the linear regression algorithm with a single variable.
  • Nowadays, there are so many advanced machine learning algorithms, libraries, and techniques available that linear regression may seem to be not important.
  • In this article, I will explain the linear regression algorithm step by step.
  • Here, ‘h’ is the hypothesis or the predicted dependent variable, X is the input feature, and theta0 and theta1 are the coefficients.
  • Then using gradient descent, we will update the theta value to minimize the cost function.
  • In this dataset, column zero is the input feature and column 1 is the output variable or dependent variable.
  • As I mentioned before, our purpose was to optimize the theta values to minimize the cost.
  • That means the theta values are optimized correctly as we expected.

save | comments | report | share on


Basic Linear Regression Algorithm in Python for Beginners

  • The most basic machine learning algorithm has to be the linear regression algorithm with a single variable.
  • Nowadays, there are so many advanced machine learning algorithms, libraries, and techniques available that linear regression may seem to be not important.
  • In this article, I will explain the linear regression algorithm step by step.
  • Here, ‘h’ is the hypothesis or the predicted dependent variable, X is the input feature, and theta0 and theta1 are the coefficients.
  • Then using gradient descent, we will update the theta value to minimize the cost function.
  • In this dataset, column zero is the input feature and column 1 is the output variable or dependent variable.
  • As I mentioned before, our purpose was to optimize the theta values to minimize the cost.
  • That means the theta values are optimized correctly as we expected.

save | comments | report | share on


I designed an AI system that can predict ‘academic dishonesty’ with marginal accuracy

  • One of the core delegations during pandemic includes avoiding social gathering which severely impacts the learning and assessment behavior of traditional classroom .
  • Artificial Intelligence (AI) techniques has gained wide popularity because of its ability to predict with marginal accuracy and complex problem solving potential .One of the several advantages of using the AI in SMART Classroom setting is its cognitive potential.
  • The concept of utilizing the architecture of AI in the SMART classroom environment will definitely improve the quality of learning in an ideal scenario.
  • To summarize, for any educational institute to become a global dominant leader at online education it needs to address the challenges on (i) timely and cost-effective improved feedback mechanism to students (ii) enhanced online proctoring mechanism to ensure academic honesty.

save | comments | report | share on


XRL: eXplainable Reinforcement Learning

  • The interpretability of the framework comes from the fact that each task (for instance stack cobblestone block) is described by human instruction, and the trained agents can only access learnt skills through these human descriptions, making the agent’s policies and decisions human-interpretable.
  • The resulting framework exhibited a higher learning efficiency, was able to generalize well in new environments and was inherently interpretable as it needs weak human supervision to give the agent instructions in order to learn new skills.
  • In Explainable Reinforcement Learning through a Causal Lens, action influence models are incorporated for Markov Decision Processes (MDP) based RL agents, extending structural causal models (SCMs) with the addition of actions.
  • Explanation generation requires the following steps: 1) defining the action influence model; 2) learning the structural equations during reinforcement learning; and finally, 3) generating explanans for explanandum.

save | comments | report | share on


Detecting Face Features with Python

  • Today we are going to learn how to work with images to detect faces and to extract facial features such as the eyes, nose, mouth, etc.
  • In the past, we have covered before how to work with OpenCV to detect shapes in images, but today we will take it to a new level by introducing DLib, and abstracting face features from an image.
  • So far we did pretty well at finding the face, but we still need some work to extract all the features (landmarks).
  • So far DLib has been pretty magical in the way it works, with just a few lines of code we could achieve a lot, and now we have a whole new problem, would it continue to be as easy?
  • Turns out DLib offers a function called shape_predictor() that will do all the magic for us but with a caveat, it needs a pre-trained model to work.

save | comments | report | share on