Overview
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CNNs, RNNs and other neural networks for unsupervised and supervised deep learning
What you'll learn:- Build and execute machine learning models on TensorFlow
- Implement Deep Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks
- Understand and implement unsupervised learning models such as Clustering and Autoencoders
TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes itto build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TFmodels at scale, and perform distributed training and prediction.
This is a comprehensive, from-the-basics course on TensorFlow and building neural networks.It assumes no prior knowledge of Tensorflow, all you need to know isbasic Python programming.
What's covered:
- Deep learningbasics: What a neuron is; how neural networks connect neurons to 'learn' complex functions; how TF makes it easy to build neural network models
- Using Deep Learning for the famous MLproblems: regression, classification, clustering and autoencoding
- CNNs -Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs
- RNNs - Recurrent Neural Networks: LSTMs, Back-propagation through time and dealing with vanishing/exploding gradients
- Unsupervised learning techniques -Autoencoding, K-means clustering, PCA as autoencoding
- Working with images
- Working with documents and word embeddings
- Google Cloud MLEngine:Distributed training and prediction of TFmodels on the cloud
- Working withTensorFlow estimators
