TensorFlow and the Google Cloud ML Engine for Deep Learning
- 4.3
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Brief Introduction
CNNs, RNNs and other neural networks for unsupervised and supervised deep learningDescription
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
Loony Corn
4.3 Raiting
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- 4.2
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- 0.0
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- 4.6
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This is the third course of the Advanced Machine Learning on GCP specialization. In this course, Prerequisites: Basic SQL, familiarity with Python and TensorFlow...
