How Machines Learn: Principles of AI learned for fun

Unveiling the Mystery: The Science Behind Machine Learning and Deep Learning

Created byAI Castle

Last updated 4/2023

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What you'll learn

Understanding the fundamental principles and algorithms of machine learning and deep learning
Acquiring methods for optimizing model performance through hyperparameter tuning
Gaining knowledge of various deep learning architectures (CNN, DNN, etc.) and their structure and operation principles
Recognizing and addressing major deep learning issues and challenges

Course content

2 sections • 15 lectures • 2h 0m total length

Section Introduction0:43
Explore how machines learn through machine learning and deep learning, understand hyperparameters, and examine DeepRacer's deep learning models to grasp practical ai principles.
Machine Learning Overview10:49
Explore how machine learning and artificial intelligence use data with supervised learning using labels, reinforcement learning with rewards, and unsupervised learning to analyze features.
How Machines Learn9:39
This lecture shows how machines learn from data, focusing on supervised learning with a linear model y = w x, training to reduce loss via optimization and updating weights.
Hyperparameters15:50
Explore how hyperparameters shape learning by comparing loss types (MSE, MAE, Huber) and using gradient descent and backpropagation with learning rate, batch size, mini-batches, and epochs.
Deep Learning and DNN9:59
Explore deep learning fundamentals with the deep neural network (DNN), including inputs, layers, parameters, activation functions (ReLU), and training to adjust weights for AI models.
CNN and DeepRacer Model11:45
Explore how convolutional neural networks use filters and pooling to convert image data into feature maps, as DeepRacer applies a three-layer CNN before final DNN outputs.
Deep Learning Issues3:48
Examine deep learning issues like overfitting and generalization loss, early stopping using validation data, the black box challenge and explainable AI, gradient vanishing and exploding, and hyperparameter tuning.

Section Introduction0:48
Explore reinforcement learning principles, training models with rewards, and how DeepRacer applies these algorithms, covering data structure, model training, and common issues and terminology.
Reinforcement Learning Overview7:19
Examine how reinforcement learning teaches machines through rewards, inspired by Skinner's operant conditioning. Observe agent-environment interactions with state, action, reward, and the reward function guiding a CNN-based policy in DeepRacer.
Goals in Reinforcement Learning13:24
Explore how reinforcement learning defines return as the discounted sum of future rewards and trains a policy to maximize G.
Types of Reinforcement Learning Algorithms8:29
Explore the two main reinforcement learning types, model-based and model-free, and delve into policy-based, value-based, and actor-critic approaches, including DeepRacer's PPO and SAC.
Data Structures in Reinforcement Learning6:23
Explores the data structure of reinforcement learning, including state, action, reward, and episodes within an environment. It shows how data forms an experience buffer for policy updates.
Training Models in Reinforcement Learning12:20
Learn how reinforcement learning trains a value model to predict returns using temporal difference learning, then updates a policy model with policy gradient, illustrated with PPO.
Challenges and Issues in Reinforcement Learning3:24
Explore key reinforcement learning challenges, including sparse rewards and unstable training. Learn how exploration and hyperparameters like entropy and SAC alpha influence learning duration and policy.
Hyperparameters and Terminology in RL5:47
Explore reinforcement learning basics by defining episodes, steps, and iterations, and learn how PPO-based hyperparameters—from gradient descent batch size to entropy and discount factor—shape exploration and convergence.

Requirements

There are no specific prerequisites for this course, as it is designed to be beginner-friendly.
However, a basic understanding of mathematics and programming concepts will be helpful to better grasp the material.
No specialized tools or equipment are required, but having a personal computer with internet access is essential to access the course materials and perform hands-on exercises.

Description

Welcome to the standalone course on "Machine Learning and Deep Learning," a unique offering that has been carefully crafted by referencing a key section of our comprehensive "Exciting AI: Autonomous Driving & RL with AWS DeepRacer" course.

In this specialized course, we will dive deep into the foundations of machine learning and deep learning, both crucial components in the development of autonomous driving technologies. The course is structured as follows:

Machine Learning Overview
How Machines Learn
Hyperparameters
Deep Learning and DNN
CNN Model
Deep Learning Issues

Whether you are a beginner or have some experience in the AI field, this course will provide you with valuable insights into the inner workings of machine learning and deep learning algorithms.

Throughout this course, you'll gain a deeper understanding of how machines learn, the role of hyperparameters, and the differences between deep learning and traditional machine learning.

You'll also explore Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and how they are applied to the DeepRacer Model. Lastly, we will discuss common issues encountered in deep learning and potential solutions.

For your information, this course is designed to provide you with a focused understanding of machine learning and deep learning concepts.

However, if you're curious about not just the theoretical aspects of AI, but also eager to dive into hands-on practice and implementing autonomous driving solutions, we highly recommend enrolling in the full "Exciting AI: Autonomous Driving & RL with AWS DeepRacer" course. By taking the complete course, you'll have the opportunity to explore AI concepts more extensively, and experience the thrill of creating your own autonomous driving models. Don't hesitate to challenge yourself and join us in the exciting world of AI and autonomous driving!

Who this course is for:

This course is designed for anyone interested in learning about machine learning and deep learning principles, regardless of their background or experience level.
It is ideal for beginners, enthusiasts, and professionals who want to gain a solid understanding of AI concepts and applications. The course is also suitable for those who wish to explore the world of AI for personal development or career advancement.

How Machines Learn: Principles of AI learned for fun

What you'll learn

Explore related topics

Course content

Machine Learning and Deep Learning7 lectures • 1hr 3min

Reinforcement Learning (Bounus)8 lectures • 58min

Requirements

Description

Who this course is for: