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Neural Networks Tutorial with Keras and TensorFlow in R Studio

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Learn Artificial Neural Networks (ANN) in R. Build predictive deep learning models using Keras and Tensorflow R Studio R interface to Keras Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. User-friendly API which makes it easy to quickly prototype deep learning models. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.


12 Best Courses to Learn Deep Learning

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A generative Adversarial Network (GAN) is a powerful algorithm of Deep Learning. Generative Adversarial Network is used in Image Generation, Video Generation, and Audio Generation. In short, GAN is a Robot Artist, who can create any kind of art perfectly. And in this Generative Adversarial Networks (GANs) Specialization, you will learn how to build basic GANs using PyTorch and advanced DCGANs using convolutional layers. You will use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation. There are 3 courses in this Specialization program where you will gain hands-on experience in GANs. Now, let's see all the 3 courses of this Specialization Program-


Deep Learning: Convolutional Neural Networks in Python

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Deep Learning: Convolutional Neural Networks in Python, Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow Created by Lazy Programmer Inc. Preview this Course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes


Complete Tensorflow 2 and Keras Deep Learning Bootcamp

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This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! Learn to use TensorFlow 2.0 for Deep Learning Leverage the Keras API to quickly build models that run on Tensorflow 2 Perform Image Classification with Convolutional Neural Networks Use Deep Learning for medical imaging Forecast Time Series data with Recurrent Neural Networks Use Generative Adversarial Networks (GANs) to generate images Use deep learning for style transfer Generate text with RNNs and Natural Language Processing Serve Tensorflow Models through an API Use GPUs for accelerated deep learning This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning!


Deep Learning: Convolutional Neural Networks in Python

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This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.