(1 Rating)

Modern AI: Applications and Overview

Categories: AI and Data Science

About Course

This course bridges theoretical knowledge with practical applications of modern AI techniques. Learners will explore neural network-based AI, gaining hands-on experience through accessible Python programming applications. The course organizes complex AI topics into an engaging structure, enriched with videos, text, and quizzes to enhance the learning journey. It is currently offered at a discounted price for early adopters. Those who enroll now will retain full access to the course, including all future updates, at no additional cost.

Course Content

What is Modern AI
The topic discusses what modern AI means.

Definition and Key Characteristics

03:39
The Evolution of AI

10:00
Ethical and Legal Challenges in AI

03:00
Quiz on Modern AI Definitions

Neural Network Basics
The topic covers basic understanding for neural networks.

Understanding Neural Networks: The Building Blocks of AI

20:00
Quiz on Neural Network basics.
Coding a Neural Network using Python Pytorch

15:00
Quiz on the Neural Network Code
Coding a Neural Network using scikit-learn — Three lines of code

08:00
Overfitting and L1, L2 Regularization Techniques

15:00
Quiz on Overfitting and Regularization
Feature Selection Using L1 Regularization

30:00
Quiz on Feature Section using L1 Regularization
Understanding the Validation Set

08:00
Quiz on Understanding the Validation Set
What is a Regression Problem vs. a Classification Problem?

05:00
Quiz on Regression Vs. Classification
Building a Neural Network for Classification

12:00
Activation Functions ReLU and Leaky ReLU

09:00
Python Coding of ReLU and Leaky ReLU

10:00
Quiz on Activation functions, ReLU, and Leaky ReLU
Sigmoid Function as an Activation Function

10:00
Quiz on Sigmoid Function
Tanh Activation Function

15:00
Quiz on tanh Activation
nn.Sequential: Simplifying Neural Network Code in PyTorch

10:00
Types of Neural Networks and When to Use Which Type

08:23

Generative AI (GenAI) Basics

What is Generative AI?

06:00
Why Generative Models Instead of Simpler Models: Distribution-based Explanation

15:00
Quiz on Generative AI Basics
Generative Adversarial Networks (GANs)

20:00
Coding for GAN using Tabular Data

25:00
Quiz on GAN
Diffusion Models: The Theory Behind Data Generation

20:00
Quiz on Diffusion
Coding a Diffusion Model

20:00
Case Study: Generate Sine Wave using a Diffusion Model

10:00

Neural Network Embeddings

Sequential Models

Intro. to Sequential Models

10:00
Quiz on Sequential Models Intro.
Recurrent Neural Networks (RNNs)

30:00
Quiz on RNN
Leveraging Pytorch’s RNN

15:00
Long Short-Term Memory (LSTM) Networks

40:00
Quiz on LSTM
Generating Text Using LSTM

20:00
Encoder-Decoder Models

20:00
Quiz on Encoder-Decoder Models
Attention Mechanisms in Sequence-to-Sequence Models

30:00
Quiz on Attention in seq2seq
Transformer: The Backbone of LLMs

30:00
Generating Text using Transformers

15:00
LLMs

15:00

Foundation Models and Fine-Tuning

Foundation Models

12:00
Are “Foundation Models” and “Pre-trained Models” the Same Concept?

05:00
Understanding BERT

25:00
More on Masking in BERT

15:00
Using BERT for Text Embeddings

20:00
BERT for Question/Answering

20:00
BART, An Encoder-Decoder Transformer Model

00:00
Using BART for Long-Text Summarization

00:00
What is Fine-Tuning?

05:00
Fine-Tuning GPT2

30:00

Commercial Models — OpenAI API

Model Combination Frameworks

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Student Ratings & Reviews

5.0

Total 1 Rating

3 Ratings

0 Rating

Maria Jimenez

7 months ago

Great explanation and material.

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About Course

What Will You Learn?

Course Content

What is Modern AI The topic discusses what modern AI means.

Definition and Key Characteristics

The Evolution of AI

Ethical and Legal Challenges in AI

Quiz on Modern AI Definitions

Neural Network Basics The topic covers basic understanding for neural networks.

Understanding Neural Networks: The Building Blocks of AI

Quiz on Neural Network basics.

Coding a Neural Network using Python Pytorch

Quiz on the Neural Network Code

Coding a Neural Network using scikit-learn — Three lines of code

Overfitting and L1, L2 Regularization Techniques

Quiz on Overfitting and Regularization

Feature Selection Using L1 Regularization

Quiz on Feature Section using L1 Regularization

Understanding the Validation Set

Quiz on Understanding the Validation Set

What is a Regression Problem vs. a Classification Problem?

Quiz on Regression Vs. Classification

Building a Neural Network for Classification

Activation Functions ReLU and Leaky ReLU

Python Coding of ReLU and Leaky ReLU

Quiz on Activation functions, ReLU, and Leaky ReLU

Sigmoid Function as an Activation Function

Quiz on Sigmoid Function

Tanh Activation Function

Quiz on tanh Activation

nn.Sequential: Simplifying Neural Network Code in PyTorch

Types of Neural Networks and When to Use Which Type

Generative AI (GenAI) Basics

What is Generative AI?

Why Generative Models Instead of Simpler Models: Distribution-based Explanation

Quiz on Generative AI Basics

Generative Adversarial Networks (GANs)

Coding for GAN using Tabular Data

Quiz on GAN

Diffusion Models: The Theory Behind Data Generation

Quiz on Diffusion

Coding a Diffusion Model

Case Study: Generate Sine Wave using a Diffusion Model

Neural Network Embeddings

What Are Neural Network Embeddings?

Generating Word Embeddings from Text using Skip-gram Model: Hands on Python Coding

Quiz on Embeddings

Is Skip-gram Model Linear?

Sequential Models

Intro. to Sequential Models

Quiz on Sequential Models Intro.

Recurrent Neural Networks (RNNs)

Quiz on RNN

Leveraging Pytorch’s RNN

Long Short-Term Memory (LSTM) Networks

Quiz on LSTM

Generating Text Using LSTM

Encoder-Decoder Models

Quiz on Encoder-Decoder Models

Attention Mechanisms in Sequence-to-Sequence Models

Quiz on Attention in seq2seq

Transformer: The Backbone of LLMs

Generating Text using Transformers

LLMs

Foundation Models and Fine-Tuning

Foundation Models

Are “Foundation Models” and “Pre-trained Models” the Same Concept?

Understanding BERT

More on Masking in BERT

Using BERT for Text Embeddings

BERT for Question/Answering

BART, An Encoder-Decoder Transformer Model

Using BART for Long-Text Summarization

What is Fine-Tuning?

Fine-Tuning GPT2

Commercial Models — OpenAI API

OpenAI Platform

Model Combination Frameworks

Model Combination

Knowledge Distillation in Modern AI: Teacher-Student Neural Networks

What is Modern AI
The topic discusses what modern AI means.

Neural Network Basics
The topic covers basic understanding for neural networks.