•TensorFlow X Recipes for Supervised and Unsupervised Learning
•The Course Overview
•Set Up and Installing TensorFlow
•Defining and Running a Computational Graph
•Visualizing a Computational Graph With TensorBoard
•How to Read Data From Files
•The Hello World of Deep Learning Your First Deep Neural Network
•Building DNN Models for Regression With TensorFlow Core
•Building DNN Models for Classification With TensorFlow Core
•Performing Regularization in DNN Models
•How to Work With Optimizers
•How to Use Keras for Building DNN
•Performing Regression with Estimators API
•Performing Classification with Estimators
•Working with Other Models from Estimators API
•Customizing DNN Models Layers Activations Optimizers and Metrics
•Building Autoencoders
•How to Perform PCA for Dimensionality Reduction
•Building a Restricted Boltzmann Machine
•How to Perform Clustering
•Asset TensorFlow X Recipes for Supervised and Unsupervised Learning
•Advanced Predictive Techniques with ScikitLearn and TensorFlow
•How Ensemble Methods Work
•Bagging Random Forests and Boosting for Regression
•Bagging Random Forests and Boosting for Classification
•Kfold CrossValidation
•Comparing Models with Kfold CrossValidation
•HyperParameter Tuning in scikitlearn
•Feature Selection Methods
•Dimensionality Reduction and PCA
•Creating New Features
•Improving Models with Feature Engineering
•Introduction to Artificial Neural Networks
•Elements of a Deep Neural Network Model
•Installation and Introduction to TensorFlow
•Core Concepts in TensorFlow
•Predictions with TensorFlow Introductory Example
•Regression Using Deep Neural Networks
•Classification with Deep Neural Networks
•TensorFlow 1.X Recipes for Supervised and Unsupervised Learning
•The Hello World of Deep Learning – Your First Deep Neural Network
•Customizing DNN Models – Layers, Activations, Optimizers and Metrics
•Asset 1- TensorFlow 1.X Recipes for Supervised and Unsupervised Learning:
•Advanced Predictive Techniques with Scikit-Learn and TensorFlow
•How Ensemble Methods Work?
•Bagging, Random Forests, and Boosting for Regression
•Bagging, Random Forests, and Boosting for Classification
•K-fold Cross-Validation
•Comparing Models with K-fold Cross-Validation
•Hyper-Parameter Tuning in scikit-learn
•Predictions with TensorFlow – Introductory Example
•Asset 2- Advanced Predictive Techniques with Scikit-Learn and TensorFlow:
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