TensorFlow and the Google Cloud ML Engine for Deep Learning

TensorFlow and the Google Cloud ML Engine for Deep Learning

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What you will 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

Curriculum

Section 1: Introduction

Section 2: Installation

Section 3: TensorFlow and Machine Learning

Section 4: Working with Images

Section 5: K-Nearest-Neighbors with TensorFlow

Section 6: Linear Regression with a Single Neuron

Section 7: Linear Regression in TensorFlow

Section 8: Logistic Regression in TensorFlow

Section 9: The Estimator API

Section 10: Neural Networks and Deep Learning

Section 11: Classifiers and Classification

Section 12: Convolutional Neural Networks (CNNs)

Section 13: Recurrent Neural Networks (RNNs)

Section 14: Unsupervised Learning

Section 15: TensorFlow on the Google Cloud

Section 16: TensorFlow Using Cloud ML Engine

Section 17: Feature Engineering and Hyperparameter Tuning

Course Description

CNN's, RNNs and other neural networks for unsupervised and supervised deep learning

Requirements

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

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

 

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