Hands-On Introduction To Artificial Intelligence(Ai)
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5:44:15
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1 - What is Artificial Intelligence AI.mp4
02:40
2 - Mapping human functions to AI technologies.mp4
02:40
3 - AI Branches of Machine Learning Algorithms.mp4
03:43
4 - AI Supervised Machine Learning Algorithms and Applications.mp4
04:40
5 - AI Unsupervised Machine Learning Algorithms and Applications.mp4
03:08
6 - AI Natural Language Processing and Applications.mp4
07:59
7 - AI Computer Vision and Applications.mp4
06:31
8 - AI IOT and Applications.mp4
06:26
9 - What are Neural Networks.mp4
02:07
10 - Neural Networks Perceptron.mp4
06:47
11 - What are Deep Neural Networks.mp4
04:52
12 - Feed Forward Neural Networks FFNN Structure and Forward pass.mp4
05:00
13 - Input Feed Forward Neural Networks FFNN.mp4
01:13
14 - Learning Phase Feed Forward Neural Networks FFNN.mp4
09:11
15 - Back propagation and learning step Feed Forward Neural Networks FFNN.mp4
05:27
16 - Applications and Limitations of Feed Forward Neural Networks FFNN.mp4
01:12
17 - CNN Introduction.mp4
02:47
18 - CNN Convolution and Relu Layer.mp4
06:33
19 - CNN Max Pooling Layer.mp4
04:07
20 - CNN Example end to end.mp4
02:39
21 - Recurrent Neural Network RNN.mp4
04:02
22 - RNN Architecture.mp4
03:20
23 - Generative Adversarial Networks GAN.mp4
03:45
24 - Reinforcement Learning.mp4
05:05
25 - Transfer Learning.mp4
04:14
26 - Market Potential of AI.mp4
03:24
27 - Who will loose to AI.mp4
07:09
28 - Need for retraining and reskilling.mp4
02:50
29 - How to take advantage and benefit from AI.mp4
04:42
30 - Building Supervised and Unsupervised Machine learning Models using IBM Watson.mp4
02:02
31 - Approach to building machine learning Models.mp4
05:02
32 - Account Setup and Configuration.mp4
04:15
33 - Supervised Building a Binary classificationML model and Uploading Data.mp4
01:57
34 - Supervised Training and testing your model using logistic regression.mp4
07:40
35 - Supervised Building a Multi class classificationML model end to end.mp4
07:14
36 - Unsupervised Building a RegressiveML Model end to end.mp4
05:12
37 - Performance Evaluation Parameters for ML Algorithms.mp4
06:14
38 - Introduction to the Section.mp4
01:47
39 - IBM Watson Text to Speech.mp4
06:03
40 - IBM Watson Speech to Text.mp4
05:27
41 - IBM Watson Semantic extraction.mp4
07:32
42 - Introduction to the Section and the experiment sheet.mp4
02:28
43 - Building a Perceptron.mp4
03:26
44 - Building a Feed Forward Neural Network with one Hidden layer Supervised.mp4
03:23
45 - Building a Deep Feed Forward Neural Network Supervised.mp4
03:26
46 - High Level Introduction to Tensor Flow Data and Setup Unsupervised.mp4
02:44
47 - Building a Regressive Feed Forward Neural NetworkFFNN Unsupervised.mp4
11:30
48 - Building a SHALLOW Regressive Feed Forward Neural Network Unsupervised.mp4
03:41
49 - Building a DEEP Regressive FFNN Unsupervised.mp4
04:19
50 - Building a Regressive FFNN with different AdamOptimizer.mp4
03:49
51 - Building a Regressive FFNN with different learning Rates and Epochs.mp4
10:10
52 - Performance Analysis of Feed Forward Neural Networks.mp4
06:05
53 - Section Introduction and data.mp4
03:55
54 - CNN for MNIST Architecture Walkthrough.mp4
01:40
55 - IBM Watson Account Setup Basics.mp4
04:05
56 - CNN Setup and First Run with MNIST example Part 1.mp4
13:00
57 - CNN Setup and First Run with MNIST example Part 2.mp4
09:20
58 - CNN for MNIST with SGD.mp4
06:19
59 - Optimizing CNN for MNIST.mp4
15:58
60 - CNN for CIFAR 10.mp4
12:24
61 - Optimization options for CNN on CIFAR 10.mp4
08:29
62 - CNN Unconverging Experiments.mp4
04:51
63 - Introduction the section.mp4
01:25
64 - Japanese Vowels classification with LSTM Walk through of Mathworks example.mp4
11:21
65 - Classification of human activities with LSTM Walk through of Mathworks example.mp4
07:49
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- language english
- Training sessions 65
- duration 5:44:15
- Release Date 2024/04/22
