Grokking Artificial Intelligence Algorithms video edition
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7:28:21
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01-Preface - Our obsession with technology and automation.mp4
05:28
02-Preface - Ethics, legal matters, and our responsibility.mp4
07:57
03-Chapter 1 Intuition of artificial intelligence.mp4
10:45
04-Chapter 1 A brief history of artificial intelligence.mp4
10:09
05-Chapter 1 Super intelligence - The great unknown.mp4
09:43
06-Chapter 1 Banking - Fraud detection.mp4
10:53
07-Chapter 2 Search fundamentals.mp4
10:16
08-Chapter 2 Representing state - Creating a framework to represent problem spaces and solutions.mp4
10:59
09-Chapter 2 Breadth-first search - Looking wide before looking deep.mp4
07:14
10-Chapter 2 Depth-first search - Looking deep before looking wide.mp4
11:28
11-Chapter 3 Intelligent search.mp4
07:03
12-Chapter 3 A search.mp4
07:37
13-Chapter 3 Use cases for informed search algorithms.mp4
05:44
14-Chapter 3 Exercise - What values would propagate in the following Min-max tree.mp4
09:27
15-Chapter 3 Alpha-beta pruning - Optimize by exploring the sensible paths only.mp4
05:07
16-Chapter 4 Evolutionary algorithms.mp4
06:47
17-Chapter 4 Problems applicable to evolutionary algorithms.mp4
10:02
18-Chapter 4 Encoding the solution spaces.mp4
08:52
19-Chapter 4 Selecting parents based on their fitness.mp4
08:59
20-Chapter 4 Two-point crossover - Inheriting more parts from each parent.mp4
08:38
21-Chapter 4 Configuring the parameters of a genetic algorithm.mp4
06:45
22-Chapter 5 Advanced evolutionary approaches.mp4
08:53
23-Chapter 5 Arithmetic crossover - Reproduce with math.mp4
08:33
24-Chapter 5 Change node mutation - Changing the value of a node.mp4
06:21
25-Chapter 6 Swarm intelligence - Ants.mp4
07:49
26-Chapter 6 Representing state - What do paths and ants look like.mp4
07:45
27-Chapter 6 Set up the population of ants.mp4
09:13
28-Chapter 6 Updating pheromones based on ant tours.mp4
08:08
29-Chapter 7 Swarm intelligence - Particles.mp4
10:33
30-Chapter 7 Problems applicable to particle swarm optimization.mp4
09:38
31-Chapter 7 Calculate the fitness of each particle.mp4
07:52
32-Chapter 7 Position update.mp4
11:12
33-Chapter 8 Machine learning.mp4
09:37
34-Chapter 8 Collecting and understanding data - Know your context.mp4
07:56
35-Chapter 8 Ambiguous values.mp4
08:51
36-Chapter 8 Finding the mean of the features.mp4
07:48
37-Chapter 8 Testing the model - Determine the accuracy of the model.mp4
07:42
38-Chapter 8 Classification with decision trees.mp4
09:57
39-Chapter 8 Decision-tree learning life cycle.mp4
09:54
40-Chapter 8 Classifying examples with decision trees.mp4
08:40
41-Chapter 9 Artificial neural networks.mp4
10:56
42-Chapter 9 Exercise - Calculate the output of the following input for the Perceptron.mp4
11:33
43-Chapter 9 Forward propagation - Using a trained ANN.mp4
08:07
44-Chapter 9 Backpropagation - Training an ANN.mp4
11:29
45-Chapter 9 Options for activation functions.mp4
07:37
46-Chapter 9 Bias.mp4
08:46
47-Chapter 10 Reinforcement learning with Q-learning.mp4
07:26
48-Chapter 10 Problems applicable to reinforcement learning.mp4
09:55
49-Chapter 10 Training with the simulation using Q-learning.mp4
11:25
50-Chapter 10 Exercise - Calculate the change in values for the Q-table.mp4
07:01
51-Chapter 10 Deep learning approaches to reinforcement learning.mp4
07:51
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- language english
- Training sessions 51
- duration 7:28:21
- Release Date 2023/11/06
