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TensorFlow and the Google Cloud ML Engine for Deep Learning

17.5 hours on-demand video

$ 12.99

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Brief Introduction

CNNs, RNNs and other neural networks for unsupervised and supervised deep learning

Description

TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models 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 is basic Python programming.

What's covered:

Deep learning basics: 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 ML problems: 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 ML Engine: Distributed training and prediction of TF models on the cloud
Working with TensorFlow estimators

Requirements

Requirements
Basic proficiency at programming in Python
Basic understanding of machine learning models is useful but not required

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