Artificial Intelligence

Deep Learning – Introduction to Boltzmann Machines

Boltzmann Machines – A kind of imaginary recurrent neural network and this normally get interpreted from the probabilistic graphical models. In a short and concise manner a  neural network which is fully connected and consist of visible and hidden units. It operates in asynchronous mode with stochastic updates for each of its unit. These machines are also called as probability distributions on high dimensional binary vectors.

 

What is Deep Learning?

“Deep learning is undeniably mind-blowing” machine learning technique that teaches computers to do what comes naturally to humans: learn by example. It can be used with ease to predict the unpredictable”. Researchers and engineers are busy in creating artificial intelligence by using a combination of non-bio-neural networks and natural intelligence”.

Deep Learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. In DL it uses many layers of nonlinear processing units for feature extraction and transformation. It has revolutionized today’s industries by demonstrating near human-level accuracy in certain tasks. The task like pattern recognition, image classification, voice/text decoding and many more.

Deep Leaning is a key technology

  • To voice control in mobile devices like handphones, TVs, vice command enabled speakers and TVs
  • Behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.
  • Has revolutionised, image processing & classification and also speech recognition with high accuracy.

Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before. Business leaders and developers community absolutely need to understand what it is, what it can do and how it works.

 

Boltzmann Machine -A Probabilistic Graphical Models

Sir Geoffrey Hinton, the “Godfather of Deep Learning” coined Boltzmann Machine in 1985 for the first time. A well known figure and personality ineep learning community  Sir Geoffrey Hinton also a a professor at the University of Toronto.

Boltzmann Machines – A kind of imaginary recurrent neural network and this normally get interpreted from the probabilistic graphical models. In a short and concise manner a  neural network which is fully connected and consist of visible and hidden units. It operates in asynchronous mode with stochastic updates for each of its unit.

These machines are also called as probability distributions on high dimensional binary vectors. Its a generative unsupervised model used for probability distribution from an original datase. A great demanding / hungry tool for computation power however restricting its network topology the behaviour can be controlled.

Its an algorithm useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. Like any other neural network these machines also have (both BM and RBM) an input layer or referred to as the visible layer and one or several hidden layers or referred to as the hidden layer.

 

Deep learning Computational Models

The human brain is a deep and complex recurrent neural network. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. In very simple words and not to confuse anything/anyone here, we can define both models as below.

  • Feedforward propagation – Type of Neural Network architecture where the connections are “fed forward” only i.e. input to hidden to output The values are “fed forward”.
  • Backpropagation (supervised learning algorithm) is a training algorithm with 2 steps:
    • Feedforward the values
    • Calculate the error and propagate it back to the layer before.

In short, forward-propagation is part of the backpropagation algorithm but comes before back-propagating.

RBM- AILabPage

 

Restricted Boltzmann Machines

Boltzmann machines are probability distributions on high dimensional binary vectors which are analogous to Gaussian Markov Random Fields in that they are fully determined by first and second-order moments.

It is used for pattern storage and retrieval. As per wiki “A Boltzmann machine is also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network and Markov random field.”

 

Books + Other readings Referred

  • Open Internet
  • Hands-on personal research work @AILabPage

 

Points to Note:

All credits if any remains on the original contributor only. We have now elaborated on our earlier posts on “AI, ML, and DL – Demystified“, for understanding Deep Learning only. You can find earlier posts on Machine Learning – The Helicopter viewSupervised Machine LearningUnsupervised Machine Learning, and Reinforcement Learning  links.

 

Feedback & Further Question

Do you have any questions about Quantum technologies, Artificial Intelligence and its subdomains like Deep Learning or Machine Learning? etc. Leave a comment or ask your question via email. Will try my best to answer it.

 

Machine Learning (ML) - Everything You Need To KnowConclusion –  Deep Learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. In DL it uses many layers of nonlinear processing units for feature extraction and transformation. in the RBM, hidden units are connected only indirectly through visible units. If the visible unit values are fixed, hidden units become independent. Learning is based on multiple levels of features or representation in each layer with the layers forming a hierarchy of low-level to high-level features Where traditional machine learning focuses on feature engineering, deep learning focuses on end-to-end learning based on raw features. Traditional deep learning creates/ train-test splits of the data where ever possible via cross-validation. Load ALL the training data into main memory and compute a model from the training data.

 

 

============================ About the Author =======================

Read about Author at : About Me

Thank you all, for spending your time reading this post. Please share your opinion / comments / critics / agreements or disagreement. Remark for more details about posts, subjects and relevance please read the disclaimer.

FacebookPage    ContactMe      Twitter         ====================================================================

Facebook Comments
Advertisements

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.