Most people are familiar with building sequential models, in which layers follow each other one by one. For instance, in a convolutional neural network, we may decide to pass images through a convolutional layer, a max pooling layer, a flattening layer, then a dense layer. These standard constructions of networks are known as'linear topologies'. However, many high-performing networks are not linear topologies, for example the Inception module, core to the top Inception model. In the module, an input from one layer is passed into four separate layers, which are concatenated back into one output layer.

In this series of articles, I would like to show how we can use a deep learning algorithm for fake news detection and compare some neural network architecture. This is the second part of this series, where I would like to create several deep learning models with Keras and Tensorflow. In the previous part of this series, I made exploratory data analysis for fake and not fake news. I used different analytics technic to compare fake and not fake news, let's give this work for neural networks. To start modeling, we need to make data preprocessing.

You can easily create learning curves for your deep learning models. First, you must update your call to the fit function to include reference to a validation dataset. This is a portion of the training set not used to fit the model, and is instead used to evaluate the performance of the model during training.

Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. They work by encoding the data, whatever its size, to a 1-D vector. This vector can then be decoded to reconstruct the original data (in this case, an image). The more accurate the autoencoder, the closer the generated data is to the original. In this tutorial we'll explore the autoencoder architecture and see how we can apply this model to compress images from the MNIST dataset using TensorFlow and Keras.

Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training. Be able to run deep learning models with Keras on Tensorflow backend Stunning SUPPORT. I answer questions on the same day. Understand how to feed own data to deep learning models (i.e.

While deep neural networks are all the rage, the complexity of the major frameworks has been a barrier to their use for developers new to machine learning. There have been several proposals for improved and simplified high-level APIs for building neural network models, all of which tend to look similar from a distance but show differences on closer examination. Keras is one of the leading high-level neural networks APIs. It is written in Python and supports multiple back-end neural network computation engines. Given that the TensorFlow project has adopted Keras as the high-level API for the upcoming TensorFlow 2.0 release, Keras looks to be a winner, if not necessarily the winner.

One of my favorite things about TensorFlow 2.0 is that it offers multiple levels of abstraction, so you can choose the right one for your project. In this article, I'll explain the tradeoffs between two styles you can use to create your neural networks. The first is a symbolic style, in which you build a model by manipulating a graph of layers. The second is an imperative style, in which you build a model by extending a class. I'll introduce these, share notes on important design and usability considerations, and close with quick recommendations to help you choose the right one.

Before we jump into coding, let's understand the problem we are trying to solve. As humans, it is very easy for us to read and recognize a bunch of handwritten digits. Since this recognition is done unconsciously, we don't realize how difficult this problem actually is. Now imagine teaching a computer how to recognize these digits and writing out a set of rules (otherwise known as an algorithm) to tell the computer how to distinguish each digit from another. This proves to be quite a difficult task! Neural networks approach the problem of digit recognition very differently.