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This explanation was fantastic! It was clear, concise, and made a potentially confusing topic incredibly simple to understand. I really appreciate the effort you put into making this video. Thank you so much!

Your videos are incredibly valuable, thank you for posting them

Golden

In 2024, the value continues to persist.

I was familiar with the concept but now I learned practically. Thanks

Dr. Soper, thank you for this excellent video Your clear and thorough explanation, along with the detailed SQL statements, made the concept so much easier to understand.

thankyou sir for your bright explaination about erd

Very simple and straightforward ✅

Best content ever on reinforcement learning

beauty!!

It 's 11 years down the line and I am finding this tutorial to be invaluable. Thanks, Dr. Soper, may God bless you abundantly.

I appreciate your careful systemic explanation

the enthusiastic voice also helps shred some of my depression crumbs.

Can I put this on my channel, please? I will mention you

WHAT AN AWESOME VIDEO

Hi, can you export the model to a sql script?

Thank you for the vids Dr. Soper, and your small laughs are cute :) :)

26:00 Metadata; Deescribe a structure of data

1:00

Extremely clear explanation for this topic. You are my life saver when I am preparing my finals. Thanks a lot.

Super duper thankful for you and all your time and effort you pour into these. Your a lifesaver. I finally understand complex topics of database!! <3 THANK YOUUU SOOOO MUCH!!!

Thank you Sir

I'm really thankful with you, I needed add a new attribute!!!

I don't believe anyone teaches it better than you. Amazing.

Thank you

Gemini: This video is about the foundations of artificial neural networks and deep Q-learning. The video starts with introducing artificial neurons and activation functions. An artificial neuron is the building block of artificial neural networks. It receives input values, multiplies each value by a weight, and sums the weighted inputs together. Then, it applies an activation function to this sum to produce an output value. There are many different activation functions, and some of the most common ones are threshold, sigmoid, hyperbolic tangent, and rectified linear unit (ReLU). Next, the video explains what a neural network is. A neural network is an interconnected collection of artificial neurons. These neurons are arranged in layers, and each neuron in one layer connects to neurons in the next layer. The information flows through the network from the input layer to the output layer. When a neural network is used for supervised learning, it is provided with a set of training examples. Each training example consists of an input value and a corresponding output value. The neural network learns by iteratively adjusting the weights of the connections between the neurons. The goal is to adjust the weights so that the network can accurately predict the output value for any given input value. The video then covers deep Q-learning, which is a combination of Q-learning and deep learning. Q-learning is a reinforcement learning method that can be used to learn a policy for an agent. In Q-learning, the agent learns a Q-value for each state-action pair. The Q-value represents the expected future reward that the agent can expect to receive if it takes a particular action in a particular state. Deep Q-learning uses a deep neural network to learn the Q-values. The input to the neural network is the state of the environment, and the output of the network is the set of Q-values for all possible actions that the agent can take in that state. Finally, the video talks about exploration in deep Q-learning. Exploration is important because it allows the agent to learn about the different states and actions that are available in the environment. In deep Q-learning, the exploration-exploitation dilemma is addressed by using a softmax function. The softmax function converts the set of Q-values for a state into a probability distribution for each possible action. The action chosen by the agent is then determined by taking a random draw from this probability distribution. This means that the agent is more likely to take the action that appears to yield the greatest reward, but it will occasionally take actions that currently appear to be suboptimal in order to try to discover new information that may yield greater overall rewards in the long run.

Gemini: This video is about a complete walkthrough of a Q-learning based AI system in Python. The video starts with an introduction to the business problem. The problem is about designing a warehouse robot that can travel around the warehouse to pick up items and bring them to a packaging area. The robot needs to learn the shortest path between all the locations in the warehouse. Then the video explains the concept of Q-learning, which is a reinforcement learning technique. Q-learning works by letting an agent learn from trial and error. The agent receives rewards for taking good actions and penalties for taking bad actions. Over time, the agent learns to take the actions that will lead to the greatest reward. Next, the video dives into the code. The code defines the environment, which includes the states, actions, and rewards. The states are all the possible locations of the robot in the warehouse. The actions are the four directions that the robot can move (up, down, left, and right). The rewards are positive for reaching the packaging area and negative for all other locations. The code also defines a Q-learning agent. The agent starts at a random location in the warehouse and then takes a series of actions. The agent learns from the rewards that it receives for its actions. Over time, the agent learns to take the shortest path to the packaging area. Once the agent is trained, the video shows how to use the agent to find the shortest path between any two locations in the warehouse. The video also shows how to reverse the path so that the robot can travel from the packaging area to any other location in the warehouse. Overall, this video is a great introduction to Q-learning and how it can be used to solve real-world problems.

Is there any guarantee that the q-values will converge during training?

Q-Learning well explained, thank you!

Thank you ❤

Gemini: This video is about Q-learning, a type of reinforcement learning. The video starts with a brief introduction to reinforcement learning. Reinforcement learning is a type of machine learning where an AI agent learns by interacting with its environment. The agent receives rewards for good actions and penalties for bad actions. The goal of the agent is to learn a policy that maximizes its total reward. Q-learning is a specific type of reinforcement learning that is used to learn policies for environments that are unknown. In Q-learning, the agent maintains a Q-table, which is a table that stores the estimated value of taking a particular action in a particular state. The agent learns by updating the Q-table based on the rewards it receives. The video then goes on to discuss the characteristics of Q-learning models. Q-learning models are finite state and finite action. This means that the number of possible states and actions that the agent can take is finite. The video also discusses two classic Q-learning problems: the maze problem and the cliff walking problem. In the maze problem, the agent is trying to find its way from the start state to the goal state. In the cliff walking problem, the agent is trying to navigate a cliff without falling off. The video then discusses Q-values. Q-values are the estimated values of taking a particular action in a particular state. The agent learns Q-values by updating the Q-table based on the rewards it receives. The video also discusses cue tables in Q-learning. Cue tables are tables that store Q-values. The Q-table has one row for each possible state and one column for each possible action. Next, the video talks about temporal differences (TDs) in Q-learning. Temporal differences are a way of measuring the difference between the expected future reward of taking an action and the immediate reward of taking that action. The video then discusses the Bellman equation, which is a fundamental equation in Q-learning. The Bellman equation is used to update the Q-value of a state-action pair based on the TD for that state-action pair. Finally, the video discusses the process of Q-learning. The process of Q-learning involves initializing the Q-table, choosing an action from the Q-table for the current state, taking that action and transitioning to the next state, receiving a reward for taking the action, computing the TD for the previous state-action pair, updating the Q-value for the previous state-action pair using the Bellman equation, and looping back to the beginning.

can some one recommend me a book to use it as a reference and study Db and SQl

always the best , but it will help if you can share a link to this slides

What a fantastic explanation. Thank you for the wonderful content.

Amazing

Great explanation for how things work internally. Thanks!

Thank you for making this available

7:17 inclusive subtype is antipattern. Supertype can ONLY have ONE subtype. David C. Hay talks about this in "Data modeling patterns" ~30 years ago.

Awesome content ,It also see you have watched young sheldon or big bang😅

It seems like textbooks intentionally try to bewilder the students rather than teach something. Your explanation is impeccable!

Thank you for creating this course! Helped a bunch.

background music in very annoying! But lesson was good

This single video has covered most of what I have learned in 14 weeks of my 16 week college database class...

Thank you, great tutorial

please avoid the background music, its hard to concentrate ... but good content overall thank you

What if you execute same statement twice?

Wow, this is an outstanding lecture!

Thanks for the videos: you definitely have a talent for explaining things. Also, you have a very pleasant voice (don't get me wrong xD), which helps to perceive new information. Thank you again and keep going with your amazing work!

Very well explained, thank you.

What app is this?