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Introduction to Modern AI

Cisco Networking Academy: Introduction to Modern AI

I have never seen such a lively course before. I have seen LinkedIn course trying hard to be lively but they often come off as creepy. This is proper lively.

1.0.

AI, Machine Learning & Models

AI: The science and engineering of making intelligent machines - John McCarthy
Intelligence: The computational part of the ability to achieve goals in the world.
Mechanical Task: The machine doesn't have to figure out how to achieve something, it gets it al.
Fewer Rules: Easier, More rules: Harder.

    AI
Symbolic AI    Machine learning
(rules based)   (based on statistics: learn from data an examples)

Machine learning is a sub-field of AI.
Machine Learning is the study of algorithms that allow computer programs to automatically improve through experience.
Model: Is a function meaning that it takes in input, does something to process that input and produces some output.
Model learns to change how it processes the input to change hte output.
Models have parameters that can be adjusted.
In ML, inputs are called features.
AI is collection of many kinds of models, for example: Structured Data, Computer Data, Automatic Speech Recognition, Audio Generation, Natural Language Processing(both the audio ones before).
Neural Network: Performs really well when it can learn with a lot of data.
Neuron is a fundamental unit for Neural Network.
Neuron contains weights(parameters).
Weight word is specific for Neural Networks.
Layer are many neurons in one group.
Deep Learning is also Deep Learning.

Training phase

input → model → output → (Comparison with correct answer)  → Performance measure → Optimizer

Inference phase→ When the training is complete, a user can use that model in the inference phase only.

input → model → output

The Big Picture:
1. AI is a field of research that seeks to enable computers to perform tasks intelligently.
2. Machine learning is a major sub-field of AI that uses data to train computers to perform tasks.
3. A model is a function that performs a task by taking an input and producing an output, and can learn from experience and data to get better at that task.
If you get stuck: Keep moving forward and see the whole course first and then get back to the course.
Give yourself permission to move forward and come back later.

1.1.

Computer Vision

1.2.

Machine Translation

1.3.

Chatbots Overview

1.4.

Ways to use Chatbots

1.5.

Generative AI and Other Vocabulary Words

2.0.