From the author of the book, Understanding Deep Learning. — <<Chapter 2 Excellence >>Chapter 4 Development Data science is a vast field. It has several topics, concepts, and methods. Call them tools. A common mistake is we try to learn the tools first. And forget that we are here to learn solving problems. …

From the author of the book, Understanding Deep Learning. — <<Chapter 1 Passion >>Chapter 3 Theory In his memoir, Matthew McConaughey speaks about a moment in his life when he was to tell his father that he wants to switch from law school to acting. He expected his intolerant father to skin him alive. But his father listened to him…

From the author of the book, Understanding Deep Learning. — >>Chapter 2 Excellence We have been pumped with the adage of the modern world, “follow your passion.” The questions are, What is passion? How to find one’s passion? While the adage is clear, the answers to these aren’t. Without the answers, I have seen data scientists pursuing an apparition confused…

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. — <<Download the free book, Understanding Deep Learning, to learn more>> Deep Learning provides a wide variety of models. With them, we can build extremely accurate predictive models. However, with the wide variety and a multitude of setting parameters it may be daunting to find a starting point.

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. — <<Download the free book, Understanding Deep Learning, to learn more>> Go to the prequel, Part I. In Part I, we learned that PCA and Autoencoders share architectural similarities. But despite this, an Autoencoder by itself does not have PCA properties, e.g. orthogonality. …

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. — <<Download the free book, Understanding Deep Learning, to learn more>> The availability of Deep Learning APIs, such as Keras and TensorFlow, have made model building and experimentation extremely easy. However, a lack of clear understanding of the fundamentals may put us in a directionless race to the best model. …

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. — <<Download the free book, Understanding Deep Learning, to learn more>> TensorFlow 2.0 is going to change the landscape of Deep Learning. It has made, model building simpler,

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. — <<Download the free book, Understanding Deep Learning, to learn more>> TensorFlow 2 is going to change the landscape of Deep Learning. It has made, model building simpler,

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. — <<Download the free book, Understanding Deep Learning, to learn more>> In my previous post, LSTM Autoencoder for Extreme Rare Event Classification [1], we learned how to build an LSTM autoencoder for a multivariate time-series data.