Brighten your Career with Data Science Certificate Courses in Bangalore

In this post, we’ll look at Keras, a popular deep learning system written in Python. Keras’ modular architecture makes dealing with deep learning extremely simple and quick. Keras handles all higher-level deep learning modeling parts of your workstation’s GPU and CPU with ease. Keras with TensorFlow is the most popular and commonly used deep learning framework, which comes as no surprise. This post will provide you with an overview of the framework so that you may start utilizing it for your deep learning experiments with confidence. After reading this article, you will have a basic understanding of Keras, such as: What is Keras and why is it so popular in the field of deep learning? Keras framework has a number of important features. Keras’s modular architecture. TensorFlow, Theano, and Microsoft’s Cognitive Technology, or CNTK in short. A quick comparison of the frameworks. So, let’s get started. Introduction to Keras It’s a Python-based open-source library for creating neural networ...

  Deep learning has revolutionized the area of Machine Learning in recent years. They're about "discovering rich (...) models" that can handle a wide range of data. So far, including a variety of well-known models into deep learning discriminative models has had significant consequences due to unique properties such as high-dimensionality mapping, intensive separate input to a class label, and the use of backpropagation and dropout methods. While most approaches have been discriminative, generative deep learning has become more popular in recent years. GAN was introduced in 2014 by pitting two neural networks against each other by Ian Goodfellow, the Godfather of GAN: a man who has given a machine the gift of imagination. The discriminatory neural network tries to determine whether the information is real or fake, while the generating neural network tries to provide data that the discriminator assumes is true. GANs, or Generative Adversarial Networks, have become quite po...

 Convolutional neural networks (CNNs) are the foundations of deep learning-based image recognition, although they only address one classification problem: They can decide if the content of a photograph may be linked to a given image class based on past instances. As a result, you may send a photo to a deep neural network that has been trained to recognise dogs and cats and get an output that tells you whether the photo contains a dog or a cat. The network outputs the chance of the photo containing a dog or a cat (the two classes you trained it to identify) and the output sums to 100 per cent if the last network layer is a softmax layer. You get scores that you can interpret as probabilities of content belonging to each class, independently, when the last layer is a sigmoid-activated layer. Understanding the concepts of probability is a must for a Machine Learning Engineer or a Data Science Professional.  The scores will not always add up to 100 per cent. When the following oc...

‘Large dataset’- the integral slice of machine learning and data science. But handling such a large quantity of data is not so easy because it crashes the RAM efficiency, leading to non-fitting the ML algorithm in the concerned system. If you got stuck with the same problem, then you have landed on the right page. This blog will help you out with the easy process of handling large datasets for machine learning with the maximum possible level of fluency. What is a large dataset? Suppose you have conducted a survey. This was needed for your market research studies. Now, obviously, the collection of responses from the survey will not be a tiny one (like 100 responses). Usually, business surveys include tons of individual responses (microdata) and many factors (dependent and independent) as raw data for data analysis and manipulations. Such an expanded set of data is called a large dataset. How does a large dataset differ from big data? As described in the above definition, the large dat...