Пікірлер

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  @kwabenalloydКүн бұрын

  Golden

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  @joseantoniocisneros78459 күн бұрын

  In 2024, the value continues to persist.

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  @muhammadafzal23711 күн бұрын

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

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  @youssefa717221 күн бұрын

  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.

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  @ismulazam763122 күн бұрын

  thankyou sir for your bright explaination about erd

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  @EmmanuelDblezd23 күн бұрын

  Very simple and straightforward ✅

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  @saeedseyedhossein959625 күн бұрын

  Best content ever on reinforcement learning

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  @zebra221828 күн бұрын

  beauty!!

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  @danmuoki278829 күн бұрын

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

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  @christineadhiambo289628 күн бұрын

  same lol😊😊

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  @danmuoki278828 күн бұрын

  @@christineadhiambo2896 wakenya jameni 😂. You taking databases 1

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  @christineadhiambo289628 күн бұрын

  @@danmuoki2788 😅😅😅 happy that i find some one we are taking this course same time. wish you luck

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  @aaronsalifukoroma7994Ай бұрын

  I appreciate your careful systemic explanation

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  @alanturing1Ай бұрын

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

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  @MacooasmeАй бұрын

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

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  @abhaychandra2624Ай бұрын

  WHAT AN AWESOME VIDEO

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  @DavidLevy-rh2osАй бұрын

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

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  @jc8345Ай бұрын

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

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  @forheuristiclifeksh7836Ай бұрын

  26:00 Metadata; Deescribe a structure of data

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  @forheuristiclifeksh7836Ай бұрын

  1:00

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  @takeiteasydragon2 ай бұрын

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

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  @bop782 ай бұрын

  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!!!

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  @mamtanarang28982 ай бұрын

  Thank you Sir

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  @emilianogomez20712 ай бұрын

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

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  @gamuchiraindawana28272 ай бұрын

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

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  @shanabenjamin89452 ай бұрын

  Thank you

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  @gemini_5372 ай бұрын

  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.

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  @gemini_5372 ай бұрын

  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.

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  @gemini_5372 ай бұрын

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

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  @gemini_5372 ай бұрын

  Q-Learning well explained, thank you!

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  @user-ge3bb9qg9j2 ай бұрын

  Thank you ❤

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  @gemini_5372 ай бұрын

  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.

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  @AbdulrahmanElsaaid2 ай бұрын

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

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  @AbdulrahmanElsaaid2 ай бұрын

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

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  @AMAN1AC282 ай бұрын

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

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  @gamuchiraindawana28272 ай бұрын

  Amazing

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  @johnkheir56352 ай бұрын

  Great explanation for how things work internally. Thanks!

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  @planetshootproduction17212 ай бұрын

  Thank you for making this available

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  @skelaw2 ай бұрын

  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.

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  @karimichristine62082 ай бұрын

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

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  @user-ls6ds1js3y2 ай бұрын

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

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  @CaliforniaBabe33 ай бұрын

  Thank you for creating this course! Helped a bunch.

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  @pammasinghkainth3 ай бұрын

  background music in very annoying! But lesson was good

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  @MontPyth3 ай бұрын

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

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  @adrianCoding3 ай бұрын

  Thank you, great tutorial

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  @user-en6mb4ew6n3 ай бұрын

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

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  @saurabhshrigadi3 ай бұрын

  What if you execute same statement twice?

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  @r.a.p.h44813 ай бұрын

  Wow, this is an outstanding lecture!

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  @user-ri5lw8qy5k3 ай бұрын

  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!

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  @deninsrmic41653 ай бұрын

  Very well explained, thank you.

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  @JoseGejunVanBoy3 ай бұрын

  What app is this?

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  @lovejoyslara83193 ай бұрын

  Great Lesson. Thank you!

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  @rjbergeriii95454 ай бұрын

  Your explanation has been of immense value to me Master Soper...I'm working on a project that utilizes Firewalls for designing a Web Infrastructure. Your video has saved me lots of resource crunching efforts and - even if I would do some crunching (wink wink) - understanding the said resources has become N times easier to be very honest. Its been 10 years and counting and your video on Firewalls still stands strong. I guess its a good indicator of the actual Firewalls you build too. Thanks once again for your time and your attention to the details of this concept. Now if you'd excuse me I'm about to re-watch and take notes ^_^