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Opening Credits
1/5/2024
2 preface
1/5/2024
3 understanding deep learning
1/5/2024
4 neural networks and backpropagation
1/5/2024
5 convolutional neural networks (cnns)
1/5/2024
6 recurrent neural networks (rnns)
1/5/2024
7 generative models and gans
1/5/2024
8 deep learning in practice
1/5/2024
9 future trends in deep learning
1/5/2024
10 applications of deep learning
1/5/2024
11 ethical considerations in deep learning
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12 popular deep learning models
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13 popular deep learning models convolutional neural network (cnn)
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14 popular deep learning models recurrent neural network (rnn)
1/5/2024
15 popular deep learning models long short term memory (lstm)
1/5/2024
16 popular deep learning models generative adversarial network (gan)
1/5/2024
17 popular deep learning models transformer
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18 popular deep learning models bert (bidirectional encoder representations from transformers)
1/5/2024
19 popular deep learning models alexnet
1/5/2024
20 popular deep learning models vggnet
1/5/2024
21 popular deep learning models resnet (residual network)
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22 popular deep learning models mobilenet
1/5/2024
23 tools and languages
1/5/2024
24 tools and languages tensorflow
1/5/2024
25 tools and languages pytorch
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26 tools and languages keras
1/5/2024
27 tools and languages caffe
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28 tools and languages scikit learn
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29 tools and languages matlab
1/5/2024
30 tools and languages mxnet
1/5/2024
31 tools and languages theano
1/5/2024
32 tools and languages onnx (open neural network exchange)
1/5/2024
33 resources and further reading
1/5/2024
34 conclusion and next steps
1/5/2024
35 glossary
1/5/2024
36 bibliography
1/5/2024
Ending Credits
1/5/2024