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Posts

  • Reparameterization layers

    In this post, we will cover how to use DenseReparameterization layer. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 24, 2021

  • DenseVariational Layers

    In this post, we will cover prior distribution over the weight and obtain posterior distribution. We will implement feed-forward network using the DenseVariational Layer. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 24, 2021

  • Probabilistic Layers

    In this post, we will introduce other probabilistic layers and how we can use them.. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 23, 2021

  • The DistributionLambda Layer

    In this post, we will introduce the most direct way of incorporating distribution object into a deep learning model with distribution lambda layer. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 19, 2021

  • Maximum Likelihood Estimation - how neural networks learn

    In this post, we will review a Maximum Likelihood Estimation (MLE for short), an important learning principle used in neural network training. This is the copy of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 19, 2021

  • Trainable Distributions

    In this post, we will take a look at how to make the parameters of distribution object trainable. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 18, 2021

  • Naive Bayes and logistic regression

    In this post, we will develop the naive bayes classifier for iris dataset using Tensorflow Probability. This is the Program assignment of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 18, 2021

  • Sampling and log probs

    In this post, we will take a look at how broadcasting rules can be applied to the prob and log_prob methods of a distribution method. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 13, 2021

  • Broadcasting Rules in Tensorflow Probability

    In this post, it will introduce you to numpy's broadcasting rules and show how you can use broadcasting when specifying batches of distributions in TensorFlow, as well as with the `prob` and `log_prob` methods. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 13, 2021

  • Independent Distribution

    In this post, we will find the meaning of the independent distribution, which is the bridge between univariate distribution and multivariate distribution. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 12, 2021

  • Multivariate Distribution

    In this post, we will build multivariate distribution. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

    Aug 11, 2021

  • Univariate Distribution

    In this post, we will show the basic usage of tensorflow probability (tfp), and how to make univariate distribution. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London

    Aug 11, 2021

  • The Power of Image Augmentation

    In this post, it will show the effect of image augmentation while training Convolutional Neural Network. And it will also show how to use `ImageDataGenerator` in Tensorflow.

    Jul 30, 2021

  • Various way of Stock Data Analysis

    In this post, we will taste various way of stock data analysis. This is the tutorial offered in Finance AI lecture from Chung-Ang University.

    Jul 28, 2021

  • Ridge-Lasso Regression

    In this post, We will review the way of generalization, especially on Ridge and Lasso.

    Jun 17, 2021