For this task, we will develop a neural network to answer the question of when the now-defunct Space Shuttle should use its autolanding system. The default decision is to let the crew land the craft. However, the autoland capability may be required for situations of crew incapacitation or adverse effects of gravity upon re-entry after extended orbital operations. This data is based on computer simulations, not actual flights. In reality, the autoland system went through some trials and tribulations and, for the most part, the shuttle astronauts were in charge during the landing process. Here are a couple of links for further background information:
Creating a simple neural network
by Dr. Sunil Kumar Chinnamgari, Cory Lesmeister
Advanced Machine Learning with R
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Preparing and Understanding Data
- Linear Regression
- Logistic Regression
- Advanced Feature Selection in Linear Models
- K-Nearest Neighbors and Support Vector Machines
- Tree-Based Classification
- Neural Networks and Deep Learning
- Creating Ensembles and Multiclass Methods
- Cluster Analysis
- Principal Component Analysis
- Association Analysis
- Time Series and Causality
- Text Mining
- Exploring the Machine Learning Landscape
- ML versus software engineering
- Types of ML methods
- ML terminology – a quick review
- Deep learning
- Big data
- Natural language processing
- Computer vision
- Cost function
- Model accuracy
- Confusion matrix
- Predictor variables
- Response variable
- Dimensionality reduction
- Class imbalance problem
- Model bias and variance
- Underfitting and overfitting
- Data preprocessing
- Holdout sample
- Hyperparameter tuning
- Performance metrics
- Feature engineering
- Model interpretability
- ML project pipeline
- Learning paradigm
- Datasets
- Summary
- Predicting Employee Attrition Using Ensemble Models
- Implementing a Jokes Recommendation Engine
- Fundamental aspects of recommendation engines
- Getting started
- Understanding the Jokes recommendation problem and the dataset
- Building a recommendation system with an item-based collaborative filtering technique
- Building a recommendation system with a user-based collaborative filtering technique
- Building a recommendation system based on an association-rule mining technique
- Content-based recommendation engine
- Building a hybrid recommendation system for Jokes recommendations
- Summary
- References
- Sentiment Analysis of Amazon Reviews with NLP
- The sentiment analysis problem
- Getting started
- Understanding the Amazon reviews dataset
- Building a text sentiment classifier with the BoW approach
- Understanding word embedding
- Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
- Building a text sentiment classifier with GloVe word embedding
- Building a text sentiment classifier with fastText
- Summary
- Customer Segmentation Using Wholesale Data
- Understanding customer segmentation
- Understanding the wholesale customer dataset and the segmentation problem
- Identifying the customer segments in wholesale customer data using k-means clustering
- Identifying the customer segments in the wholesale customer data using DIANA
- Identifying the customer segments in the wholesale customers data using AGNES
- Summary
- Image Recognition Using Deep Neural Networks
- Credit Card Fraud Detection Using Autoencoders
- Automatic Prose Generation with Recurrent Neural Networks
- Winning the Casino Slot Machines with Reinforcement Learning
- Creating a Package
- Other Books You May Enjoy
Creating a simple neural network
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