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Artificial Intelligence | GenAI | Course | ChatBot | ChatGPT

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Artificial Intelligence | GenAI | Course | ChatBot | ChatGPT, available at $54.99, has an average rating of 4.83, with 100 lectures, 10 quizzes, based on 12 reviews, and has 1074 subscribers.

You will learn about Understand How Neural Networks Work (Theory and Applications) Understand How Convolutional Networks Work (Theory and Applications) Understand how the Backpropagation algorithm works Understand Weight Initialization and Regularization Techniques Understand Loss Functions in Neural Networks Visualize the Learning Process of Neural Networks Build handwritten digit recognition AI with feedforward network Build handwritten digit image classification using CNN AI Build a Chatbot with Attention Build a Chatbot using Transformers Architecture Use OpenAI's open source GPT2 Finetune your own GPT2 for Q&A just like ChatGPT This course is ideal for individuals who are Beginner Python programmer who wants learn everything about AI and GenAI or Anyone who in interested in learning about Neural Networks and Deep Learning or Anyone who wants to start their AI and GenAI journey It is particularly useful for Beginner Python programmer who wants learn everything about AI and GenAI or Anyone who in interested in learning about Neural Networks and Deep Learning or Anyone who wants to start their AI and GenAI journey.

Enroll now: Artificial Intelligence | GenAI | Course | ChatBot | ChatGPT

Summary

Title: Artificial Intelligence | GenAI | Course | ChatBot | ChatGPT

Price: $54.99

Average Rating: 4.83

Number of Lectures: 100

Number of Quizzes: 10

Number of Published Lectures: 100

Number of Published Quizzes: 10

Number of Curriculum Items: 110

Number of Published Curriculum Objects: 110

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understand How Neural Networks Work (Theory and Applications)
  • Understand How Convolutional Networks Work (Theory and Applications)
  • Understand how the Backpropagation algorithm works
  • Understand Weight Initialization and Regularization Techniques
  • Understand Loss Functions in Neural Networks
  • Visualize the Learning Process of Neural Networks
  • Build handwritten digit recognition AI with feedforward network
  • Build handwritten digit image classification using CNN AI
  • Build a Chatbot with Attention
  • Build a Chatbot using Transformers Architecture
  • Use OpenAI's open source GPT2
  • Finetune your own GPT2 for Q&A just like ChatGPT

Who Should Attend

  • Beginner Python programmer who wants learn everything about AI and GenAI
  • Anyone who in interested in learning about Neural Networks and Deep Learning
  • Anyone who wants to start their AI and GenAI journey

Target Audiences

  • Beginner Python programmer who wants learn everything about AI and GenAI
  • Anyone who in interested in learning about Neural Networks and Deep Learning
  • Anyone who wants to start their AI and GenAI journey

Welcome to the Complete Artificial Intelligence Bootcamp with ChatBot and ChatGPT in Python using PyTorch! This course is your one-stop-shop for mastering artificial intelligence, deep learning, and PyTorch. Whether you’re a beginner or an experienced professional, this comprehensive bootcamp is designed to elevate your skills and give you a competitive edge in the AI field.

What makes this course unique?

  • Exclusive Content: Dive into materials and hands-on projects that are not available anywhere else online.

  • Comprehensive Curriculum: Covering everything from the basics of PyTorch to advanced topics like transformer architecture and ChatBot creation.

  • Hands-on Projects: Apply your learning with practical, real-world projects designed to reinforce each concept.

  • Expert Instruction: Learn from an instructor with extensive experience in AI and deep learning.

  • Interactive Quizzes: Test your knowledge with quizzes that challenge your understanding of each section.

Course Overview:

  • Section 1: Introduction

    • Get an overview of the course structure and objectives.

  • Section 2: Introduction to PyTorch

    • Learn about computational graphs, tensors, and tensor operations.

    • Dive into tensor datatypes, math operations, and shape manipulation.

    • Understand autograd and perform in-place operations.

  • Section 3: Loss Functions in Deep Learning

    • Explore various loss functions such as L1, L2, binary cross-entropy, and KL divergence.

  • Section 4: Different Activation Functions in Deep Learning

    • Understand the importance of activation functions like ReLU, Leaky ReLU, and PReLU.

  • Section 5: Normalization and Regularization

    • Learn about regularization techniques and normalization methods.

  • Section 6: Optimization in AI

    • Master optimization techniques including gradient descent and mini-batch SGD.

  • Section 7: Building a Neural Network in PyTorch

    • Step-by-step guide to designing, training, and testing a neural network using the MNIST dataset.

  • Section 8: Custom PyTorch Dataset and Dataloader

    • Create and utilize custom datasets and dataloaders for efficient data processing.

  • Section 9: Building an Image Classification CNN Model

    • Build, train, and visualize a CNN model for handwritten digit classification.

  • Section 10: Building a ChatBot using Transformer Architecture

    • Comprehensive guide to understanding and implementing transformer architecture for ChatBot development.

  • Section 11: Building a ChatBot using Pre-Trained ChatGPT

    • Comprehensive guide to understanding and fine tuning pre-trained ChatGPT for question and answer (Q&A) purpose.

Why Enroll?

By the end of this course, you will:

  • Have a deep understanding of AI and deep learning fundamentals.

  • Be proficient in using PyTorch for various machine learning tasks.

  • Be able to build and deploy neural networks and transformer models.

  • Have the skills to create a functional ChatBot.

This course is perfect for:

  • Aspiring data scientists and machine learning engineers.

  • Software developers looking to transition into AI roles.

  • Professionals seeking to enhance their AI skillset.

  • Enthusiasts eager to learn about cutting-edge AI technologies.

Enroll now and gain an unfair advantage in the AI industry with exclusive content and practical experience that sets you apart from the rest!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Quick Tips

Lecture 3: General framework for building AI model

Chapter 2: Introduction to PyTorch

Lecture 1: Introduction and Pre Requisites

Lecture 2: Computational Graph

Lecture 3: Introduction to Tensors

Lecture 4: Datatypes in Tensors

Lecture 5: Tensor Math Operations

Lecture 6: Tensor Aggregations Functions

Lecture 7: Tensor Shape Manipulation

Lecture 8: Rand vs RandN

Lecture 9: Zeros, Ones, and Likes

Lecture 10: Inplace Operations

Lecture 11: Autograd in PyTorch

Chapter 3: Loss Functions in Deep Learning

Lecture 1: Why do we need loss functions

Lecture 2: Understanding L2 Loss

Lecture 3: Understanding L1 loss

Lecture 4: L1 vs L2 Loss

Lecture 5: Understanding Binary Cross Entropy (BCE) Loss

Lecture 6: Understanding Cross Entrophy Loss

Lecture 7: Understanding Softmax

Lecture 8: Understanding KL Divergence Loss

Lecture 9: Is KL Divergence is BCE?

Chapter 4: Different Activation functions in Deep Learning

Lecture 1: Introduction to Activation Functions in Deep Learning

Lecture 2: What is Softmax

Lecture 3: Understanding TanH Activation Funtion

Lecture 4: Understanding ReLU Activation Function

Lecture 5: Understanding PReLU Activation Function

Lecture 6: Understanding Leaky ReLU Activation Function

Chapter 5: Normalization and Regularization

Lecture 1: Introduction

Lecture 2: Understanding L1 and L2 Regularization

Lecture 3: Understanding Dropouts in Neural Network

Lecture 4: Standardization and Normalization

Lecture 5: Batch Nomalization

Lecture 6: Layer Normalization

Chapter 6: Optimization in AI

Lecture 1: Introduction to Optimization in AI models

Lecture 2: Understanding Gradient Decent

Lecture 3: Mini Batch SGD

Lecture 4: Understanding Exponentially Weighted Average (EWA)

Chapter 7: Building a Neural Network in pytorch

Lecture 1: Introduction

Lecture 2: Deeper dive to MNIST Dataset

Lecture 3: MNIST Dataset Visualization

Lecture 4: Designing the Neural Network

Lecture 5: Visualizing the Neural Network Model

Lecture 6: Designing Loss Function for our Task

Lecture 7: Designing Optimizer for our Task

Lecture 8: Training our AI Model

Lecture 9: Testing (Inferencing) our trained AI Model

Lecture 10: Deep Dive into the Result Metrics

Chapter 8: Custom Pytorch Dataset and Dataloader

Lecture 1: Introduction

Lecture 2: Understanding the Data

Lecture 3: Coding in Google Colab

Lecture 4: Understanding Dataset Class

Lecture 5: Dataset in Action with example

Lecture 6: Understanding Dataloader for batch processing

Lecture 7: Using Dataloader in a Sample CNN model

Lecture 8: Running Data through the Model

Chapter 9: Building Image Classification for | Handwritten Digits | CNN Model

Lecture 1: Introduction

Lecture 2: Understanding ResNet Model

Lecture 3: Intution behind CNN Model

Lecture 4: Understanding Kernal/Filter in CNN

Lecture 5: Understanding Stride in CNN

Lecture 6: Understanding Model Architecture

Lecture 7: Understanding Pooling in CNN

Lecture 8: Coding the CNN Model

Lecture 9: Designning the Neural Network (CNN)

Lecture 10: Visualizing the CNN Model

Lecture 11: Training CNN Model

Lecture 12: Visualizing the training loop

Lecture 13: Testing/Inferencing CNN Model

Chapter 10: Building a ChatBot using Transformer Architecture

Lecture 1: Introduction

Lecture 2: Transformer Architecture

Lecture 3: The Problem Statement

Lecture 4: Key Features of Transformer Architecture

Lecture 5: Input Embeddings

Lecture 6: Positional Encoding

Lecture 7: Multi-Head Attention

Lecture 8: Scaled Dot Product and Residual

Lecture 9: Feed Forward in Transformer Architecture

Lecture 10: Decode in Transformer

Lecture 11: The Loss Function

Lecture 12: The Optimizer Class

Instructors

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  • 5 stars: 11 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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