It can bring the power of image recognition to CRM and third-party applications so that end users across sales, service, and marketing can discover new insights about their customers and predict outcomes that lead to smarter decisions.
2. Salesforce Einstein Vision By Chandan Panigrahy
Contents
● Prerequisite knowledge on AI
○ Introduction to AI
○ Goals of AI
○ Machine Learning
○ Deep Learning
● Einstein Vision
○ Introduction
○ Einstein Vision
○ Einstein Vision Terminology
3. Introduction to AI
The science and engineering of making intelligent machines, especially intelligent computer programs.
- John McCarthy (Father of AI)
Goals of AI -
● To Create expert systems
● To implement human intelligence in machines
4. Basic Terminologies of AI
Machine Learning - Machine learning is a type of AI that provides computers with the ability to learn
without being explicitly programmed.
Neural Network - A neural network is a set of algorithms designed to recognize patterns.
Deep Learning - Deep learning is a branch of machine learning, where we create and train a neural
network in a specific way.
Layer structure of Deep Learning:
● Top Layer: here the network trains on a specific set of features and then sends that information to
the next layer
● The network takes that information, combines it with other features and passes it to the next layer,
and so on.
7. Einstein Vision
Introduction -
It can bring the power of image recognition to CRM and third-party applications so that end users across
sales, service, and marketing can discover new insights about their customers and predict outcomes that
lead to smarter decisions.
Einstein Vision -
It contains a new set of APIs that will bring deep learning to developers with Predictive Vision Services,
which classify unstructured images against a library of pre-trained classifiers.
Where we use -
Visual search — Expand the ways that your customers can discover your products and increase sales.
Brand detection — Monitor your brand across all your channels to increase your marketing reach and
preserve brand integrity.
Product identification — Increase the ways that you can identify your products to streamline sales
processes and customer service.
8. Einstein Vision Terminology
1. Dataset - The training data, which consists of inputs and outputs.
2. Label - A group of similar data inputs in a dataset that your model is trained to recognize. Label
references the output name you want your model to predict.
3. Model - The model predicts which class a new input falls into based on the predefined classes
specified in your training dataset.
4. Training - The process through which a model is created and learns the classification rules based on
a given set of training inputs (dataset).
5. Prediction - The results that the model returns as to how closely the input matches data in the
dataset.