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Using Retrieval Augmented Generation with a Large Language Model AI Chatbot

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Lesson extensions

Lessons List | 30 Lesson

Five Steps to Create a New AI Model

Five Steps to Create a New AI Model

How Large Language Models Work

How Large Language Models Work

Why Are There So Many Foundation Models

Why Are There So Many Foundation Models

What are Convolutional Neural Networks CNNs

What are Convolutional Neural Networks CNNs

What are GANs Generative Adversarial Networks

What are GANs Generative Adversarial Networks

What are Transformers Machine Learning Model

What are Transformers Machine Learning Model

What is LSTM Long Short Term Memory

What is LSTM Long Short Term Memory

What is NLP Natural Language Processing

What is NLP Natural Language Processing

NLP vs NLU vs NLG

NLP vs NLU vs NLG

What is Random Forest

What is Random Forest

What are Autoencoders

What are Autoencoders

What is a Knowledge Graph

What is a Knowledge Graph

What is Monte Carlo Simulation

What is Monte Carlo Simulation

Overfitting Underfitting and Bad Data Are Ruining Your Predictive Models

Overfitting Underfitting and Bad Data Are Ruining Your Predictive Models

Gradient Descent Explained

Gradient Descent Explained

What is an RBM Restricted Boltzmann Machine

What is an RBM Restricted Boltzmann Machine

Fluid vs Crystallized Intelligence

Fluid vs Crystallized Intelligence

Edge AI vs Distributed AI

Edge AI vs Distributed AI

Use AI Powered Automation to Accelerate Auto Claims Processing

Use AI Powered Automation to Accelerate Auto Claims Processing

Supervised vs Unsupervised Learning

Supervised vs Unsupervised Learning

What is Time Series Analysis

What is Time Series Analysis

What is MLOps

Large Language Models Are Zero Shot Reasoners

Large Language Models Are Zero Shot Reasoners

What is Back Propagation

What is Back Propagation

Training AI Models with Federated Learning

Training AI Models with Federated Learning

What is PyTorch Machine Deep Learning

What is PyTorch Machine Deep Learning

Scaling AI Model Training and Inferencing Efficiently with PyTorch

Scaling AI Model Training and Inferencing Efficiently with PyTorch

How to Add AI to Your Apps Faster with Embedded AI

How to Add AI to Your Apps Faster with Embedded AI

Using Retrieval Augmented Generation with a Large Language Model AI Chatbot

Using Retrieval Augmented Generation with a Large Language Model AI Chatbot

Open Source in Action with watsonx

Open Source in Action with watsonx

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Course Description

Back propagation course, in this course we will delve deep into the intricacies of back propagation, understanding its principles, and mastering its application. We will explore how back propagation enables neural networks to learn from data by iteratively adjusting weights to minimize error. Through a series of modules, you will learn the mathematical foundations of back propagation, including the chain rule and gradient descent optimization. We will cover advanced techniques for optimizing back propagation, such as momentum, learning rate schedules, and regularization. Additionally, you will gain practical experience implementing back propagation algorithms in Python, allowing you to train and fine-tune neural networks effectively. Whether you're new to neural networks or seeking to enhance your understanding of back propagation, this course will equip you with the knowledge and skills needed to tackle complex machine learning tasks with confidence.

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