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AI world with AR
India
เข้าร่วมเมื่อ 23 ก.ย. 2020
Hello friends! I am Aanal. I welcome you to my channel! Here on this channel, you can find videos of artificial intelligence from basics to advance topics including machine learning, deep learning, natural language processing, computer vision, bandit algorithms, soft computing, etc.
If you are a learner or someone interested in AI, either in basic topics or advanced ones, subscribe this channel to explore artificial intelligence.
You can also feedback me regarding suggestion of new topics to explore.
If you are a learner or someone interested in AI, either in basic topics or advanced ones, subscribe this channel to explore artificial intelligence.
You can also feedback me regarding suggestion of new topics to explore.
วีดีโอ
Associative Memory Networks | AI
มุมมอง 9253 ปีที่แล้ว
associative-memory-networks auto-associative-memory-networks hetero-associative-memory-networks linear-and-bidirectional-associative-memory-networks brain-state-in-a-box
Boltzmann learning | Artificial Intelligence
มุมมอง 5343 ปีที่แล้ว
Neural-Network-learning-mechanisms Boltzmann-machines energy-function, visibility-of-nodes
Learning Rules/Mechanisms of Neural Network
มุมมอง 2183 ปีที่แล้ว
error-correction-learning/widrow-hoff-rule/delta-rule memory-based-learning/outstar-learning perceptron-learning competitive-learning Hebbian-learning Thanks for watching Please Subscribe, Like and Share.
Perceptron and its learning | Artificial Intelligence
มุมมอง 3063 ปีที่แล้ว
what is perceptron? unit function/ Heaviside step function (with shift ) linearly separable problem concept of perceptron classification in context of cosine angle perceptron learning perceptron learning rule perceptron update rule polar co ordinates
Selection of loss functions in NN | Artificial Intelligence
มุมมอง 1203 ปีที่แล้ว
how to select loss function in neural network? mean-squared-error/Quadratic-loss/L2-loss mean-squared-logarithmic-error Huber-loss mean-absolute-error/L1-loss Hinge-loss/multiclass-SVM-loss mean-bias-error cross-entropy-loss/negative-log-likelihood binary-cross-entropy KL-divergence-loss Jenson-Shannon-divergence-loss categorical-cross-entropy Sparse-CCE
Usage of activations functions | Artificial Intelligence
มุมมอง 1413 ปีที่แล้ว
usage of different activation functions according to requirement. When to use which activation function? binary step function linear activation sigmoid/logistic-activation-function hyperbolic-tangent tanh-function softmax function softsign function Rectified-linear-unit ReLU function Exponential-learning-unit ELU function Leaky-Relu, paramatric-relu, Swish, Gelu-(Gaussian)-function Thanks for w...
Working of Neural Network, bias in NN | Artificial Intelligence
มุมมอง 3K3 ปีที่แล้ว
how-neural-network-works what-is-artificial-neuron working-of-NN-with-example calculations-of-neural-network bias-in-NN-with-example what-is-action-function-in-NN how-bias-in-NN-works @8:13 addition/improvement/specifically: "or to bring values to desired range either -1,1 or 0,infinity, etc." Please SUBSCRIBE, LIKE and SHARE.
Neural network architecture and types | Artificial Intelligence
มุมมอง 2823 ปีที่แล้ว
neural-network-architecture learning-in-neural-network components-of-neural-network types-of-neural-network feed-forward-network-types single-layer-neural-network multi-layer-neural-network recurrent-neural-networks working-of-RNN types-of-RNN RNN-types-with-example Thanks for watching Please SUBSCRIBE, LIKE and SHARE
Backpropagation and learning process in Neural network | Artificial Intelligence
มุมมอง 2063 ปีที่แล้ว
backpropagation-in-AI learning-process-in-neural-network forward-learning-in-NN neural-network-learning Thanks for watching Please LIKE, SUBSCRIBE and SHARE
Artificial neuron | MC-Culloh Pit's model | artificial intelligence
มุมมอง 2283 ปีที่แล้ว
artificial-neuron-in-ANN Mc-culloh-pit's-model architecture-of-artificial-neuron weighted-sum-in-artificial-neuron bias-artificial-neuron threshold-artificial-neuron states-artificial-neuron Please SUBCRIBE, LIKE and SHARE
alpha-beta pruning with example in Artificial Intelligence
มุมมอง 3.8K3 ปีที่แล้ว
alpha-beta-pruning-with-solved-example optimized-minimax-algorithm adversarial-search-AI Please SUBSCRIBE, LIKE and SHARE
Adversarial Search | minimax algorithm | Artificial Intelligence
มุมมอง 7K3 ปีที่แล้ว
adversarial-search minimax-algorithm-with-example adversarial-search-problem-formulation adversarial-search-elements adversarial-search-format adversarial-search-characteristics gaming gaming-types-of-information Thanks for watching Please SUBSCRIBE, LIKE and SHARE
Bayesian Belief Network | Artificial Intelligence
มุมมอง 1.2K3 ปีที่แล้ว
bayesian-belief-network with example in AI uncertain-knowledge-representation-technique
Knowledge Representation in uncertainty- Naive Bayes theorem | Artificial Intelligence
มุมมอง 7653 ปีที่แล้ว
Knowledge Representation in uncertainty- Naive Bayes theorem | Artificial Intelligence
Backward chaining | Artificial Intelligence
มุมมอง 3593 ปีที่แล้ว
Backward chaining | Artificial Intelligence
Inference Engine: Forward Chaining | Artificial Intelligence
มุมมอง 4263 ปีที่แล้ว
Inference Engine: Forward Chaining | Artificial Intelligence
synchronic rules for inference | Artificial-Intelligence
มุมมอง 2913 ปีที่แล้ว
synchronic rules for inference | Artificial-Intelligence
Higher-order-logic representation | Artificial Intelligence
มุมมอง 1.6K3 ปีที่แล้ว
Higher-order-logic representation | Artificial Intelligence
Wumpus world problem | Artificial Intelligence
มุมมอง 3.7K3 ปีที่แล้ว
Wumpus world problem | Artificial Intelligence
Skolemization | Artificial intelligence
มุมมอง 20K3 ปีที่แล้ว
Skolemization | Artificial intelligence
Resolution-in-First-order-logic | steps-in-resolution | Artificial-intelligence
มุมมอง 2K3 ปีที่แล้ว
Resolution-in-First-order-logic | steps-in-resolution | Artificial-intelligence
Unification in FOL | Algorithm and examples. #UnificationInFOL#UnificationExamples#unificationInAI
มุมมอง 1.4K3 ปีที่แล้ว
Unification in FOL | Algorithm and examples. #UnificationInFOL#UnificationExamples#unificationInAI
Unification-in-first-order-logic | Artificial Intelligence #unification#FirstOrderLogic#AIBasics
มุมมอง 3.9K3 ปีที่แล้ว
Unification-in-first-order-logic | Artificial Intelligence #unification#FirstOrderLogic#AIBasics
Inference-in-First-order-logic | Artificial Intelligence #InferenceInFol#FirstOrderLogic#AIBasics
มุมมอง 7403 ปีที่แล้ว
Inference-in-First-order-logic | Artificial Intelligence #InferenceInFol#FirstOrderLogic#AIBasics
First-order-logic in KR| syntax and semantics| Artificial Intelligence #FirstOrderLogic#SyntaxInFOL
มุมมอง 4.8K3 ปีที่แล้ว
First-order-logic in KR| syntax and semantics| Artificial Intelligence #FirstOrderLogic#SyntaxInFOL
Rules of Inference | AI | logic in AI #RulesOfInference#LogicInAI
มุมมอง 1.3K4 ปีที่แล้ว
Rules of Inference | AI | logic in AI #RulesOfInference#LogicInAI
Propositional logic-characteristics, syntax and limitations of PL|AI #PropositionalLogic#properties
มุมมอง 1.2K4 ปีที่แล้ว
Propositional logic-characteristics, syntax and limitations of PL|AI #PropositionalLogic#properties
Knowledge representation and reasoning-types, cycle, requirements, techniques, approach, issues-KRR
มุมมอง 4.2K4 ปีที่แล้ว
Knowledge representation and reasoning-types, cycle, requirements, techniques, approach, issues-KRR
Hi
Why the added predicate and how do I know when to add predicate?
@@CajethanUchechukwu Predicate describes relationships between objects or properties of objects. It is necessary when you want to represent more complex statements than what propositional logic can handle. Propositional logic can only express simple true or false values, but FOL allows for more detailed and structured representations. To know when to add predicate: 1. Identifying Relationships ("John is taller than Mary.", represents the "taller than" relationship.), 2. To describe a property ( " ocean is blue"). 3 when there are quantifiers ( ∀, ∃) .4. while there are classes ( all humans are mortal). 5. Verb structure ( as given in video)
Nice explanation
Thanks
thank u so much maam
tomorow is my exam andi have seen a lot of videos but your video will be very helpful to me plz pray for me and thanks a lot mam
You are great, dont stop.
Thank you ❤
thanks ( Student, Mam)
dislikes(Student, video)
Fuck (student,mam)
camera aur thoda hilayiyo
You could have explained it better. Just a suggestion
In Book It Is Wrong Mam Please Answer Me
(row, col)
Numbering Is Correct
Thanks for the explanation
Draw the network and explain it sister
thanks
Really good explanation. This is the simplest video i ever saw i didn't understood after watching 2-3 videos of 10-20min also
TEACHING (mam, good)
Video bnate time fan off kr diya kro.... Sound issue . . . .
How did we get 0.72 ?
Given data, from table of " N2 calls"
N2 calls as well as alarm rang
great video keep it up☺🤗
Extreme background noise😪
very helpful tysm🤗
Thanks a lot mam.
Thank you. Miss❤️
Thank you ma'am aam 💯
WHAT ABOUT PATH ABDG DO WE HAVE TO CHECK IF IT OPTIMAL ?
In case 2, path ABDG will never be selected, 1) as f(n) value of B was greater 2) condition of underestimation doesn't satisfy for path ACEG, at node E. So no need to check for it
@@aiworldwithar8444 and why is that when B is greater , algorithm doesn't check for other path?
jo bhi FOL pe video mil rahe sab Semantics kehte hai batayenge lekin intro ke baad naam tak nhi le rahe Semantics hai kya akhir
semantics is logic
Please wear a headset microphone when recording in a noisy place 🙃
Very well explained!
Tq☺️
Understandable
Thx di
Helpful video, I cleared my doubts of this topic.👍
Truely Helpful...👍
Short and straight to the point
Good explanation 👍 I had some doubts in this topic but now cleared.
very well explained 👍
👍👍
Very good explanation, Keep it up...👍
Congratulations Beta
0:58 If you are from USA, Canada, Australia, NZ, or EU I will setup your blog and setup it to earn money w h a t s a p p +7 9 6 7 1 5 7 0 5 8 1