CNNs, RNNs and other neural networks for unsupervised and supervised deep learning
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.
- 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
Who this course is for:
- Developers who want to understand and build ML and deep learning models in TensorFlow
- Data scientists who want to learn cutting edge TensorFlow technology
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