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  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 modelling 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 utilising 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 li...

  Deep learning has revolutionised 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. The scores will not always add up to 100 per cent. When the following occurs in either situation, the classification may fail: The main item isn't what you taught the network to identify, such as...

  Data science careers are buzzing everywhere, and so the data science courses. It's true that data science salaries are too lucrative and offer sample scopes of career growth. But the majority of candidates struggle a lot to grab the right data science job after competing in their data science courses. After Bengaluru,  Mumbai, Hyderabad, and Chennai, Delhi will be the next promising destination for data science aspirants. In this blog, I'll discuss the best strategy for grabbing the right data science job in Delh i and a brief understanding of the growth orientation of the data science salary in India . Is data science a good career in India? We always keep our concerned eyes on the 1st world countries job market and keep regretting the lack of opportunities in our own country. In some cases, this becomes a very hard truth that our country lacks job opportunities and growth, but if it comes to data science, then India is also proudly participating in the data science advanc...