Introduction to Deep Learning /
Adrean, Kurt.
Introduction to Deep Learning / Kurt Adrean. - Gotland : Lynas Publishing, 2023. - 163 p. : ill. ; 23 cm.
1. License
2. Deep Learning Tutorials
3. Getting started
4. Classifying MNIST digits using Logistics Regression
5. Multiplayer Perceptron
6. Convolution Neural Networks (LeNet)
7. Denoising Autoencoders (dA)
8. Stacked Denoising Autoencoders (dA)
9. Restricted Boltzman Machine (RBM)
10. Deep Belief Networks
11. Hybrid Monte-Carlo Sampling
12. Recurrent Neural Networks with Word Embeddings
13. LSTM Networks for Sentiment Analysis
14. Modelling and Generating sequences of polyphonic music with RNN-RBM
15. Miscellaneous
Index
The book guides you to deep learning through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a long time artificial intelligence researchers specializing in natural -language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamentals concepts and techniques. Students and practitioners learn the basic of deep learning by working through programs in Tensorflow, an open-source machine learning framework.
9789188364807
LSTM
Boltzman Machine
Logistics Regression
Perceptron
Convolution Neural Networks (LeNet)
MNIST
006.31 / ADR
Introduction to Deep Learning / Kurt Adrean. - Gotland : Lynas Publishing, 2023. - 163 p. : ill. ; 23 cm.
1. License
2. Deep Learning Tutorials
3. Getting started
4. Classifying MNIST digits using Logistics Regression
5. Multiplayer Perceptron
6. Convolution Neural Networks (LeNet)
7. Denoising Autoencoders (dA)
8. Stacked Denoising Autoencoders (dA)
9. Restricted Boltzman Machine (RBM)
10. Deep Belief Networks
11. Hybrid Monte-Carlo Sampling
12. Recurrent Neural Networks with Word Embeddings
13. LSTM Networks for Sentiment Analysis
14. Modelling and Generating sequences of polyphonic music with RNN-RBM
15. Miscellaneous
Index
The book guides you to deep learning through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a long time artificial intelligence researchers specializing in natural -language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamentals concepts and techniques. Students and practitioners learn the basic of deep learning by working through programs in Tensorflow, an open-source machine learning framework.
9789188364807
LSTM
Boltzman Machine
Logistics Regression
Perceptron
Convolution Neural Networks (LeNet)
MNIST
006.31 / ADR