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
Duración:00:01:25
2 preface
Duración:00:02:52
3 understanding deep learning
Duración:00:04:53
4 neural networks and backpropagation
Duración:00:03:46
5 convolutional neural networks (cnns)
Duración:00:03:27
6 recurrent neural networks (rnns)
Duración:00:05:53
7 generative models and gans
Duración:00:03:41
8 deep learning in practice
Duración:00:03:52
9 future trends in deep learning
Duración:00:04:27
10 applications of deep learning
Duración:00:03:53
11 ethical considerations in deep learning
Duración:00:03:57
12 popular deep learning models
Duración:00:00:55
13 popular deep learning models convolutional neural network (cnn)
Duración:00:05:18
14 popular deep learning models recurrent neural network (rnn)
Duración:00:04:17
15 popular deep learning models long short term memory (lstm)
Duración:00:04:39
16 popular deep learning models generative adversarial network (gan)
Duración:00:03:58
17 popular deep learning models transformer
Duración:00:03:49
18 popular deep learning models bert (bidirectional encoder representations from transformers)
Duración:00:03:21
19 popular deep learning models alexnet
Duración:00:04:06
20 popular deep learning models vggnet
Duración:00:03:21
21 popular deep learning models resnet (residual network)
Duración:00:04:06
22 popular deep learning models mobilenet
Duración:00:03:55
23 tools and languages
Duración:00:01:15
24 tools and languages tensorflow
Duración:00:03:46
25 tools and languages pytorch
Duración:00:04:21
26 tools and languages keras
Duración:00:01:53
27 tools and languages caffe
Duración:00:03:57
28 tools and languages scikit learn
Duración:00:00:40
29 tools and languages matlab
Duración:00:01:58
30 tools and languages mxnet
Duración:00:03:30
31 tools and languages theano
Duración:00:03:38
32 tools and languages onnx (open neural network exchange)
Duración:00:03:57
33 resources and further reading
Duración:00:03:13
34 conclusion and next steps
Duración:00:03:28
35 glossary
Duración:00:03:13
36 bibliography
Duración:00:11:41
Ending Credits
Duración:00:01:54