Deep Learning course in collaboration with AWS and Facebook AI
Deep Learning, Jupyter Notebooks, CNNs, GANs, Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks
ABOUT THIS DEEP LEARNING NANODEGREE
Deep Learning, also called a deep neural network or deep neural learning is one of the fast-growing artificial intelligence functions. It drives advances in artificial intelligence that are changing our world. Become a world-class expert with a deep learning course from Udacity.
This Deep Learning course will help you build and apply neural networks in image recognition, recurrent networks for sequence generation, and how to deploy models accessible from a website. Apply style transfer to images, and learn how to use development tools such as Jupyter notebooks and Anaconda.
Build your first network with NumPy and Python. Build multi-layer neural networks using the modern deep learning framework PyTorch and analyze real data. Build convolutional networks, and use them to classify images (faces, melanomas, etc.) based on patterns and objects. Use networks to learn data image denoising and data compression. Learn how to build your own recurrent networks and memory networks with PyTorch. Discover how to use recurrent networks to generate new text from TV scripts and perform sentiment analysis.
Meet Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs. They will teach you how to generate realistic images by implementing a Deep Convolutional GAN (generative adversarial network). Build and deploy your own PyTorch sentiment analysis model and create a gateway for accessing it from a website.
What is Deep Learning?
Deep learning is a subset of machine learning (ML) that replicates the human brain’s ability to discover correlations and patterns by data processing according to a predefined logical framework. Deep learning, sometimes known as deep neural networks, employs several hidden layers in the neural network, as compared to typical neural networks, which have just a few hidden layers.
To provide an accurate output, deep learning algorithms link inputs to previously learned data. The underlying notion of this technology is quite similar to how human brains operate (biological neural networks). We arrive at a conclusion by comparing fresh information to previously collected data.
Deep Learning models are taught using vast amounts of labeled data and neural network designs that automate feature extraction without requiring human extraction.
The share of jobs requiring AI skills has grown 4.5x since 2013. The average salary of a Deep Learning Engineer in the US is $131k.
More Deep Learning courses.
Only logged in customers who have purchased this product may leave a review.