Deep Learning
Et Tu Code
This comprehensive audiobook provides an introduction to the fundamentals of deep learning, a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The book covers the key concepts, techniques, and applications of deep learning, including the basics of neural networks and backpropagation, CNNs, RNNs, generative models, and ethical considerations.
With a focus on practical applications, the book explores the use of deep learning in various fields such as computer vision, natural language processing, and robotics. It also discusses future trends in deep learning, including advancements in hardware, software, and algorithms.
Each chapter is designed to build upon previous ones, making it easy for readers to follow along and gain a deeper understanding of the subject matter. The book includes summaries, examples, and exercises to help reinforce key concepts and apply them to real-world scenarios.
Duration - 2h 16m.
Author - Et Tu Code.
Narrator - Helen Green.
Published Date - Saturday, 06 January 2024.
Copyright - © 2013 Et Tu Code ©.
Location:
United States
Description:
This comprehensive audiobook provides an introduction to the fundamentals of deep learning, a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The book covers the key concepts, techniques, and applications of deep learning, including the basics of neural networks and backpropagation, CNNs, RNNs, generative models, and ethical considerations. With a focus on practical applications, the book explores the use of deep learning in various fields such as computer vision, natural language processing, and robotics. It also discusses future trends in deep learning, including advancements in hardware, software, and algorithms. Each chapter is designed to build upon previous ones, making it easy for readers to follow along and gain a deeper understanding of the subject matter. The book includes summaries, examples, and exercises to help reinforce key concepts and apply them to real-world scenarios. Duration - 2h 16m. Author - Et Tu Code. Narrator - Helen Green. Published Date - Saturday, 06 January 2024. Copyright - © 2013 Et Tu Code ©.
Language:
English
Opening Credits
Duration:00:01:25
2 preface
Duration:00:02:52
3 understanding deep learning
Duration:00:04:53
4 neural networks and backpropagation
Duration:00:03:46
5 convolutional neural networks (cnns)
Duration:00:03:27
6 recurrent neural networks (rnns)
Duration:00:05:53
7 generative models and gans
Duration:00:03:41
8 deep learning in practice
Duration:00:03:52
9 future trends in deep learning
Duration:00:04:27
10 applications of deep learning
Duration:00:03:53
11 ethical considerations in deep learning
Duration:00:03:57
12 popular deep learning models
Duration:00:00:55
13 popular deep learning models convolutional neural network (cnn)
Duration:00:05:18
14 popular deep learning models recurrent neural network (rnn)
Duration:00:04:17
15 popular deep learning models long short term memory (lstm)
Duration:00:04:39
16 popular deep learning models generative adversarial network (gan)
Duration:00:03:58
17 popular deep learning models transformer
Duration:00:03:49
18 popular deep learning models bert (bidirectional encoder representations from transformers)
Duration:00:03:21
19 popular deep learning models alexnet
Duration:00:04:06
20 popular deep learning models vggnet
Duration:00:03:21
21 popular deep learning models resnet (residual network)
Duration:00:04:06
22 popular deep learning models mobilenet
Duration:00:03:55
23 tools and languages
Duration:00:01:15
24 tools and languages tensorflow
Duration:00:03:46
25 tools and languages pytorch
Duration:00:04:21
26 tools and languages keras
Duration:00:01:53
27 tools and languages caffe
Duration:00:03:57
28 tools and languages scikit learn
Duration:00:00:40
29 tools and languages matlab
Duration:00:01:58
30 tools and languages mxnet
Duration:00:03:30
31 tools and languages theano
Duration:00:03:38
32 tools and languages onnx (open neural network exchange)
Duration:00:03:57
33 resources and further reading
Duration:00:03:13
34 conclusion and next steps
Duration:00:03:28
35 glossary
Duration:00:03:13
36 bibliography
Duration:00:11:41
Ending Credits
Duration:00:01:54