R: Artificial Neural Nets in R - Beginner to Expert!: 3-in-1
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- Implement supervised and unsupervised machine learning in R for neural networks.
- Predict and classify data automatically using neural networks.
- Align your data science strategy with current and future systems in their respective ecosystem.
- Visualize leakages in your organization and fix them with deep learning.
- Predict outcomes of processes and propose improvements.
- Explore the Perceptron classifier
- Stack with a neural network
- Evaluate and fine-tune the models you build.
- Work with neurons, perceptron, bias, weights, and activation functions
- Gain a practical understanding of the architectures required to develop business use cases
The aim of this video is to study Neural Nets from Scratch?
Why from Scratch?
Understand the leaky abstractions behind neural networks
We will continue to explore the iris dataset further by focusing on the first two features (sepal length and sepal width), optimizing the decision tree, and creating some visualizations.
View the data with pandas
Select the best performing tree with the best_estimator_ attribute
Visualize the tree with graphviz
Decision trees for regression are very similar to decision trees for classification. The procedure for developing a regression model consists of four parts: First Load the dataset, then split the set into training/testing subsets, after that instantiate a decision tree regressor and train it, and finally score the model on the test subset.
Create a decision tree regressor, instantiate the decision tree and train it
Measure the model's accuracy
Use an error metric to compare y_test (ground truth) and y_pred (model predictions)
Ensemble algorithms use several algorithms together to improve predictions. A random forest is a mixture of several decision trees, where each tree provides a single vote toward the final prediction. The final random forest calculates a final output by averaging the results of all the trees it is composed of.
Import and instantiate a random forest
Measure prediction error
Use the estimators_attribute
It does not necessarily involve trees. It builds several instances of a base estimator acting on random subsets of the first training set. Bagging estimators are great for reducing the variance of a complex base estimator.
Import BaggingRegressor and KNeighborsRegressor
Instantiate the KNeighboursRegressor class and pass it as the base_estimator within BaggingRegressor
Look at the best parameters in the random search run
Here we will Focus on important parameters in the gradient boosting algorithm, Create a parameter distribution where the most important parameters are varied, Perform a random grid search and Use the best parameters from the previous step with many estimators.
Load the gradient boosting algorithm and random grid search
Create a parameter distribution for the gradient boosting trees
Run the grid search to find the best parameters
The fundamental process in the stacking aggregator is that we use the predictions of several machine learning algorithms as input for the training of another machine learning algorithm.
Using RandomizedSearchCV, find the best parameters
Train the best parameter set with more estimators
Predict the target using X_stack using both algorithms
QDA is the generalization of a common technique such as quadratic regression. It is simply a generalization of a model to allow for more complex models to fit. SGD is a fundamental technique used to fit a model for regression. There are natural connections between SGD for classification or regression.
Learn a nonlinear LDA
Use SGD for classification
Naive Bayes is a really interesting model. It's somewhat similar to KNN in the sense that it makes some assumptions that might oversimplify reality, but still it performs well in many cases.
Pre-process the data into a bag-of-words matrix
Fire up the classifier and fit our model
Rename the sets bow and newgroups.target to X and y respectively
Label propagation is a semi-supervised technique that makes use of labeled and unlabeled data to learn about unlabeled data. Quite often, data that will benefit from a classification algorithm is difficult to label.
Update y with -1
Use the LabelPropagation method to predict the labels
Measure the accuracy score
The two most common meta-learning methods are bagging and boosting. Stacking is less widely used; yet it is powerful because one can combine models of different types.
Split the dataset into training and testing sets
Split the training set into two sets
Train base learners on the first part of the training set
Here we are going to do some work towards building our own scikit-learn estimator. The custom scikit-learn estimator consists of at least three methods: Init initialization method, fit method and predict method.
Load the breast cancer dataset from scikit learn
Split the data into training and testing sets
Import BaseEstimator and ClassifierMixin from sklearn.base and pass them along as arguments to your new classifier
- Basic understanding of Python and R (statistical background plus a basic knowledge) would be useful to anyone taking this course.
- A sound knowledge of Anaconda (and its libraries such as NumPy and scikit-learn) is required.
Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a wide range of problems in different areas of AI and machine learning. The advantage of neural network is that it is adaptive in nature. It learns from the information provided, i.e. trains itself from the data, which has a known outcome and optimizes its weights for a better prediction in situations with unknown outcome.
R provides this machine learning environment under a strong programming platform, which not only provides the supporting computation paradigm but also offers enormous flexibility on related data processing. The open source version of R and the supporting neural network packages are very easy to install and also simple to learn. Machine learning is widely used in many areas, ranging from the diagnosis of diseases to weather forecasting. You can also experiment with any novel example, which you feel can be interesting to solve using a neural network.
This comprehensive 3-in-1 course is a step-by-step guide to understanding Neural Networks with R; throughout the course, practical, real-world examples help you get acquainted with the various concepts of Neural Networks. Develop a strong background in neural networks with R, to implement them in your applications. Learn how to build and train neural network models to solve complex problems. Implement solutions from scratch, covering real-world case studies to illustrate the power of neural network models.
Contents and Overview
This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Getting Started with Neural Nets in R, covers building and training neural network models to solve complex problems. This course explains the niche aspects of neural networking and provides you with a foundation from which to get started with advanced topics by implementing them in R. This course covers an introduction to neural nets, the R language, and building neural nets from scratch- with R packages; specific worked models are applied to practical problems such as image recognition, pattern recognition, and recommender systems. At the end of the course, you will learn to implement neural network models in your applications with the help of practical examples from companies using neural nets.
The second course, Create Your Own Sophisticated Model with Neural Networks, covers one-stop solution to learning complex models with Neural Networks and understanding the basics of Natural Language Processing. With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. Focus on Decision Trees and Ensemble Algorithms. Use scikit-learn to classify text and Multiclass with scikit-learn. Explore various algorithms for classification. Look at Naive Bayes model and Label Propagation. Finally, you'll use Neural Networks using different Classifiers and create your own Simple Estimator.
The third course, Deep Learning Architecture for Building Artificial Neural Networks, covers an introduction to deep learning and its architectures with real-world use cases. The course starts off with an introduction to Deep Learning and the different tools, hardware, and software before we begin to understand the different training models. We then get to what everyone is talking about: Neural Networks. Understand how Neural Networks work and the benefits they offer for supervised and well as unsupervised learning before building our very own neural network. Explore the different Deep Learning Architectures, including how to set up your architecture and align the output. Finally we take a look at Artificial Neural Networks and understand how to build your own ANN.
Taking this course will help you dive head first into the popular field of deep learning as a career choice or for further learning.
By the end of the course, you’ll develop a strong background in neural networks with R, to build and train neural network models and solve complex problems.
About the Authors
● ArunKrishnaswamyhas over 18 years of experience with large datasets, statistical methods, machine learning and software systems. He is one of the First Hadoop Engineers in the world, Advisor to AI Startups. He has 15+ years’ experience using R. He is also a Ph.D. in Statistics/Math with MS in CS. Expertise in Machine Learning, Neural Nets, and Deep Learning. Deep Experience in AWS, Spark, Cassandra, MongoDB, SQL, NoSQL, Tableau, R, Visualization. Data Science Mentor at UC Berkeley, Stanford, Caltech.Guest Lecturer at Community Colleges. Data Science in different domains o Fintech (Lending Club), o Cybersecurity (VISA) o Advertising Technology (Yahoo / Microsoft) o Bot Technology (voicy .ai) o Retail (WRS) o IOT (GE) o ERP (SAP) o Health Care (Blue Cross).
● Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab. He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.
● Anshul is a global technology leader who has been instrumental in driving technology transformations for business revenues in the range of multi Billion USD. His experience has been in taking up strategic technology initiatives, architecting, delivering, and managing them at an enterprise level. Anshul has several notable career accomplishments, wherein he has led, created, and launched key ecommerce, mobile, and business intelligence initiatives for the world's #1 insurance brand AXA, in the fastest growing emerging markets of Asia. He is currently in a leadership role as the Chief Information Officer Information Technology and Digital Officer, leading the IT Strategy, Technology Transformations, Analytics, software delivery, architecture and Cloud for Union Insurance (UAE, Oman and Bahrain) across all lines of business (Life, General (P&C ) and Health Insurance) Creating and Driving big strategic Initiatives aligning IT transformation to deliver business value. Major Cloud transformations impacted the bottom line of Union by multimillion AED in the first year. Transformation on Digital added multimillion revenues in Life, P&C and Health lines of business. Machine Learning, Deep Learning and Robotic Process Automation are some key business transformations implemented recently. He is a transformational leader and Senior Management IT professional, with almost two decades of experience spread across multiple geographies (US, Europe, Southeast Asia, and the Middle East). Anshul has built and led local, regional, and global teams across 3 continents, and capitalized on opportunities to drive revenues, profits, and growth. Strong P&L management. Anshul has been mentoring startups, management students (IIM Bangalore), and incubators/accelerators such as Astrolabs Dubai, T Labs, CH9, Flat 6 labs, and other incubators since 2012. Startups mentored are in the tech space of analytics, mobility, tech-based microfinance institutions, healthcare tech and analytics, and tech-based retail merchandising, logistics and mobile wallets spread across geographies including Singapore, Dubai and Middle East, India and Europe. Anshul is a speaker on Blockchain, Internet of Things (IoT), Artificial Intelligence, Machine and Deep Learning, Digital Transformation, Cloud and Mobility. He won a couple of Star Performer of the Year Awards from AXA India & AXA Asia. Three times he has been awarded the AXA innovation award both at AXA Asia and AXA Group level.
-CIO of the year award, InfoSec Maestro Award, CSO Next of the year award.
-CISO award from MESA Dubai.
-CIO award from CNME Dubai.
Anshul writes on innovation, big data and technology transformations, Blockchain and had a couple of articles published in CNME, Innovation and Tech Middle East, Dataquest, LinkedIn, and PM Network.
- Anyone who wants to work with neural networks to understand real-world examples. If you are interested in artificial intelligence and deep learning and you want to level up, then this course is what you need!
- Programmers, analysts, architects, data scientists, or anyone working in a big data environments and interested in learning how to implement Artificial intelligence in the world of Data Science. Taking this course will help you to take a first step in designing your career as a deep learning architect.