Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
Learn how to use DataGenerators in Keras for image processing related applications and Computer Vision applications of Deep Learning
In this step by step tutorial, we will be learning how to build an AI which can play the Rock Paper Scissors Game with the user by detecting and recognising the user's move using Deep Learning with a Convolutional Neural Network called SqueezeNet.
This tutorial shows how to adapt the Mask R-CNN GitHub project for training and inference using TensorFlow 2.0 and Keras.
In this tutorial you will learn how to implement Generative Adversarial Networks (GANs) using Keras and TensorFlow.
Thousands of CSV files, Keras and TensorFlow. That’s how real machine learning looks like! I hope that I will save you time telling how to train NNs using generators, tf.data.Dataset, and other pretty interesting stuff.
Top 15 Machine Learning Frameworks for AI & ML Experts: Amazon Machine Learning, Apache SINGA, TensorFlow, Scikit-Learn, MLlib Spark, Spark ML, Caffe, H2O, Torch, Keras, mlpack, Azure ML Studio, Google Cloud ML Engine, Theano, Veles
This article focuses on giving the readers some basic understanding of the Variational Autoencoders and explaining how they are different from the ordinary autoencoders in Machine Learning and Artificial Intelligence. Unlike vanilla autoencoders(like-sparse autoencoders, de-noising autoencoders .etc), Variational Autoencoders (VAEs) are generative models like GANs (Generative Adversarial Networks). This article is primarily focused on the Variational Autoencoders and I will be writing soon about the Generative Adversarial Networks in my upcoming posts. Introducing Variational AutoEncoders and Image Generation with Keras
In this article, we will try to understand the Model Sub-Classing API and Custom Training Loop from Scratch in TensorFlow 2. It may not be a beginner or advance introduction but aim to get rough intuition of what they are all about. Model Sub-Classing and Custom Training Loop from Scratch in TensorFlow 2
Neural Network uses optimising strategies like stochastic gradient descent. We can actually compute the error by using a Loss Function
Deep learning is a broader field of machine learning, which uses artificial neural networks(ANN) to derive high-level features from the inputs. Deep learning or deep neural networks(DNN) architecture consists of multiple layers, specifically the hidden layers between the input and output layers.
Simple Neural Network is feed-forward wherein info information ventures just in one direction.i.e. the information passes from input layers to hidden layers finally to the output layers. Recurrent Neural Network is the advanced type to the traditional Neural Network. It makes use of sequential information. Unlike conventional networks, the output and input layers are dependent on each other. RNNs are called recurrent because they play out a similar undertaking for each component of an arrangement, with the yield being relied upon the past calculations.LSTM or Long Short Term Memory are a type of RNNs that is useful in learning order dependence in sequence prediction problems. In this article, we will cover a simple Long Short Term Memory autoencoder with the help of Keras and python. Introduction to LSTM Autoencoder using Keras
Keras Deep Learning library helps to develop the neural network models fast and easy. There are two ways to build a model in Keras — Sequential and Functional. Keras Model Sequential API VS Functional API
One-Class Neural Network in Keras, we present a One-Class Convolutional Neural Network architecture that merges the power of deep networks to extract meaningful data representations along with the one-class objective, all in one-step.
In this article, I’m using Keras (https://keras.io/) for exploring layer implementation and source code, but in general, most types of layers are quite generic and the main principles don’t depend that much on the actual library implementing them.
In this post, we will cover the differences between a Fully connected neural network and a Convolutional neural network. We will focus on understanding the differences in terms of the model architecture and results obtained on the MNIST dataset.
In this post, we are going to re-play the classic Multi-Layer Perceptron. Most importantly, we will play the solo called backpropagation, which is, indeed, one of the machine-learning standards.
I decided to put my neural network prediction model to the test by seeing if it could pick grade A loans better than LendingClub. I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions.
Guide on how to prepare data for model training for Kaggle’s Histopathologic Cancer Detection. We are provided with a dataset of images on which we are supposed to create an algorithm (it says algorithm and not explicitly a machine learning model
In this article, I will be exploring two Word Embeddings: 1. Training our Own Embedding; 2. Pre-trained GloVe Word Embedding
A step-by-step guide of building a 1D CNN model and using data augmentation methods to classify eight classes of emotions.