
Explore semantic search, where meaning guides results beyond string matching, using knowledge graphs and models like sentence BERT to build semantically aware apps in Jupyter notebooks.
Discover how to build a semantic search engine using Sentence BERT and apply image processing and natural language processing techniques, including document classification and elastic search.
Learn how words become numbers as vectors in word embeddings, with examples like i eat banana, and see how machines represent text and images as arrays for neural networks.
Explain one-hot encoding to represent words as numeric vectors from a small corpus, showing how each word maps to a unique vector of corpus size and yields a sparse representation.
Explore deep neural networks and how AI uses machine learning to learn. Learn about logistic regression, activation functions like sigmoid, and forward propagation and back propagation to minimize error.
Explore word2vec techniques to convert words into vectors using continuous bag of words and skip-gram models, training neural networks to capture word context and relationships.
Discover how GloVe creates dense word embeddings from a co-occurrence matrix in an unsupervised setting, using weighting and a loss objective to capture word relationships and enable sentiment analysis.
Discover how BERT embeddings use bidirectional transformers and transfer learning to deliver powerful language representations via encoder and decoder self-attention and masked training.
Explore sentence transformers, a BERT-based modification that generates sentence embeddings for semantic search and clustering, using siamese and triplet networks with cosine similarity.
Obtain the semantic search source code from the GitHub repo by cloning or downloading a zip, then run the notebook and web app to compare queries with headlines.
Explore semantic search with sentence embeddings in a Jupyter notebook, embedding headlines into vectors and using cosine similarity to retrieve the top three semantically related articles.
Build a semantic search web application using sentence transformer models and Flask, load the data, compute cosine similarity, and display the top results in the user interface.
Explore semantic search with sentence BERT that captures intent and meaning beyond lexical matches, aided by a knowledge graph. Build a Python Flask app to find similar headlines.
Course Description
Learn to build semantic search engine detection engine with sentence BERT
Build a strong foundation in Semantic Search with this tutorial for beginners.
Understanding of semantic search
Learn word embeddings from scratch
Learn limitation of BERT for sentences
Leverage sentence BERT for finding similar news headlines
Learn how to represent text as numeric vectors using sentence BERT embeddings
User Jupyter Notebook for programming
Build a real life web application or semantic search
A Powerful Skill at Your Fingertips Learning the fundamentals of semantic search puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation.
No prior knowledge of word embedding or BERT is assumed. I'll be covering topics like Word Embeddings , BERT , Glove, SBERT from scratch.
Jobs in semantic search systems area are plentiful, and being able to learn it with BERT will give you a strong edge. BERT is state of art language model and surpasses all prior techniques in natural language processing.
Semantic search is becoming very popular. Google, Yahoo, Bing and Youtube are few famous example of semantic search systems in action. Semantic search engines are vital in information retrieval . Learning semantic search with SBERT will help you become a natural language processing (NLP) developer which is in high demand.
Content and Overview
This course teaches you on how to build semantic search engine using open source Python and Jupyter framework. You will work along with me step by step to build following answers
Introduction to semantic search
Introduction to Word Embeddings
Build an jupyter notebook step by step using BERT
Build a real world web application to find similar news headlines
What am I going to get from this course?
Learn semantic search and build similarity search engine from professional trainer from your own desk.
Over 10 lectures teaching you how to build similarity search engine
Suitable for beginner programmers and ideal for users who learn faster when shown.
Visual training method, offering users increased retention and accelerated learning.
Breaks even the most complex applications down into simplistic steps.
Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.