From the author of the book, Understanding Deep Learning.


From the author of the book, Understanding Deep Learning.


From the author of the book, Understanding Deep Learning.

  • What is passion?
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Understand Passion


In this article, we will get a starting point to build an initial Neural Network. We will learn the thumb-rules, e.g. the number of hidden layers, number of nodes, activation, etc., and see the implementations in TensorFlow 2.

Photo by Dewang Gupta on Unsplash


In continuation of Part I, here we will define and implement custom constraints for building a well-posed Autoencoder. A well-posed Autoencoder is a regularized model that improves the test reconstruction error.

  1. Tied weights,
  2. Orthogonal weights,
  3. Uncorrelated features, and
  4. Unit Norm.
  • implement custom layer and constraints to incorporate them.
  • demonstrate how they work, and the improvements in reconstruction…


Here we will learn the desired properties in Autoencoders derived from its similarity with PCA. From that, we will build custom constraints for Autoencoders in Part II for tuning and optimization.


Due to its ease-of-use, efficiency, and cross-compatibility TensorFlow 2.0 is going to change the landscape of Deep Learning. Here we will learn to install and set it up. We will also implement MNIST classification with TF 2.0.

  • model building simpler,
  • production deployment on any platform more robust, and
  • enables powerful experimentation for research.
  • TF 1.x also supports Keras, but in 2.0 Keras is integrated tightly with the rest of the TensorFlow platform. 2.0 …


Due to its ease-of-use, efficiency, and cross-compatibility TensorFlow 2 is going to change the landscape of Deep Learning. Here we will learn to install and set it up. We will also implement the MNIST classification with TensorFlow 2.

  • model building simpler,
  • production deployment on any platform more robust, and
  • enables powerful experimentation for research.
  • TensorFlow 1.x also supports Keras, but in 2.0 Keras is integrated tightly with the rest of the TensorFlow platform. 2.0 …


Here we will break down an LSTM autoencoder network to understand them layer-by-layer. We will go over the input and output flow between the layers, and also, compare the LSTM Autoencoder with a regular LSTM network.


Here we will learn the details of data preparation for LSTM models, and build an LSTM Autoencoder for rare-event classification.

  • data preparation steps…

Chitta Ranjan

Director of Science at ProcessMiner | Book Author | www.understandingdeeplearning.com

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