Artificial intelligence introduction
For Theory Section: Some knowledge in algebra and calculus.
For Hands-On Section: Gmail account to use Google Colab and Google Drive. Basic programming knowledge using Python 3
If you have no programming background, you may still take the course. You can still benefit from the mathematical understanding of the subject. You can play with all the code that is provided.
All codes are provided for you to download.
No software needed to install on your computer. We will use Google Colab with step-by-step set up instructions included in the course.
Description
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.
Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.
The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.
Hand-on examples are available for you to download.
Please watch the first two videos to have a better understanding of the course.
TOPICS COVERED
What is Machine Learning?
Linear Regression
Steps to Calculate the Parameters
Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function
Logistic Regression: Classification
Decision Boundary
Sigmoid Function
Non-Linear Decision Boundary
Logistic Regression: Gradient Descent
Gradient Descent using Mean Squared Error Cost Function
Problems with MSE Cost Function for Logistic Regression
In Search for an Alternative Cost-Function
Entropy and Cross-Entropy
Cross-Entropy: Cost Function for Logistic Regression
Gradient Descent with Cross Entropy Cost Function
Logistic Regression: Multiclass Classification
Introduction to Neural Network
Logical Operators
Modeling Logical Operators using Perceptron(s)
Logical Operators using Combination of Perceptron
Neural Network: More Complex Decision Making
Biological Neuron
What is Neuron? Why Is It Called the Neural Network?
What Is An Image?
My “Math” CAT. Anatomy of an Image
Neural Network: Multiclass Classification
Calculation of Weights of Multilayer Neural Network Using Backpropagation Technique
How to Update the Weights of Hidden Layers using Cross Entropy Cost Function
Hands On
Google Colab. Setup and Mounting Google Drive (Colab)
Deep Neural Network (DNN) Based Image Classification Using Google Colab. & TensorFlow (Colab)
Introduction to Convolution Neural Networks (CNN)
CNN Architecture
Feature Extraction, Filters, Pooling Layer
Hands On
CNN Based Image Classification Using Google Colab & TensorFlow (Colab)
Methods to Address Overfitting and Underfitting Problems
Regularization, Data Augmentation, Dropout, Early Stopping
Hands On
Diabetes prediction model development (Colab)
Fixing problems using Regularization, Dropout, and Early Stopping (Colab)
Hands On: Various Topics
Saving Weights and Loading the Saved Weights (Colab)
How To Split a Long Run Into Multiple Smaller Runs
Functional API and Transfer Learning (Colab)
How to Extract the Output From an Intermediate Layer of an Existing Model (Colab), and add additional layers to it to build a new model.
Who this course is for:
Who is this course for? Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on.
People from a diverse range of professions may find this knowledge useful in their own profession.
Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.
The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.
Please watch the first two videos to have a better understanding of the course.
Artificial Intelligence Tutorial
Artificial Intelligence Tutorial | AI Tutorial
The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc.
Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts.
What is Artificial Intelligence?
In today’s world, technology is growing very fast, and we are getting in touch with different new technologies day by day.
Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc.
AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human.
Introduction to AI
Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines “man-made,” and intelligence defines “thinking power”, hence AI means “a man-made thinking power.”
So, we can define AI as:
“It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions.”
Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems
With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI.
It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans.
Why Artificial Intelligence?
Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI:
With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc.
With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc.
With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk.
AI opens a path for other new technologies, new devices, and new Opportunities.
Goals of Artificial Intelligence
Following are the main goals of Artificial Intelligence:
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it’s so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics
Introduction to AI
Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
Disadvantages of Artificial Intelligence
Every technology has some disadvantages, and thesame goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI:
High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements.
Can’t think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed.
No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken.
Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities.
No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative.
Prerequisite
Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily:
Any computer language such as C, C++, Java, Python, etc.(knowledge of Python will be an advantage)
Knowledge of essential Mathematics such as derivatives, probability theory, etc.
Audience
Our AI tutorial is designed specifically for beginners and also included some high-level concepts for professionals.
Problems
We assure you that you will not find any difficulty while learning our AI tutorial. But if there any mistake, kindly post the problem in the contact form.
Artificial cybernet updates. info 