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Branch
CSE
Semester
7
Subject
Deep Learning
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What's inside: Complete exam-oriented notes for BCS714A – Deep Learning, VTU 2022 Scheme, 7th Semester.
This Study Bundle covers all 5 modules as per the VTU syllabus.
Module 1:
Introducing Deep Learning, Biological and Machine Vision, Neocognitron, LeNet-5, ImageNet, ILSVRC, AlexNet, TensorFlow Playground, Deep Learning for NLP, One-Hot Representation, Word Vectors and Word-Vector Arithmetic.
Module 2:
Regularization for Deep Learning, Parameter Norm Penalties, Dataset Augmentation, Noise Robustness, Semi-Supervised Learning, Multi-Task Learning, Early Stopping, Sparse Representations and Optimization for Training Deep Models.
Module 3:
Convolutional Neural Networks, Convolution Operation, Pooling, Variants of Convolution, Structured Outputs, Data Types, Efficient Convolution Algorithms and History of CNNs.
Module 4:
Sequence Modelling, Unfolding Computational Graphs, Recurrent Neural Networks, Bidirectional RNNs, Encoder-Decoder Sequence-to-Sequence Architecture, Deep Recurrent Networks, Recursive Neural Networks and LSTM.
Module 5:
Interactive Applications of Deep Learning, NLP Preprocessing, word2vec, Word Embeddings, ROC-AUC, IMDb Film Review Classification, Dense Networks, CNNs for NLP, RNN, LSTM, Bidirectional LSTM, Stacked Recurrent Models, Seq2seq, Attention and Transfer Learning in NLP.
Features:
• Simple English and easy-to-understand explanations
• Point-wise exam-oriented answers
• Clean diagrams and important equations
• Quick revision points
• Important VTU questions
• High-priority exam topics
• Suitable for Internal Assessment and SEE preparation
Prepared by Search Creators.
This Study Bundle covers all 5 modules as per the VTU syllabus.
Module 1:
Introducing Deep Learning, Biological and Machine Vision, Neocognitron, LeNet-5, ImageNet, ILSVRC, AlexNet, TensorFlow Playground, Deep Learning for NLP, One-Hot Representation, Word Vectors and Word-Vector Arithmetic.
Module 2:
Regularization for Deep Learning, Parameter Norm Penalties, Dataset Augmentation, Noise Robustness, Semi-Supervised Learning, Multi-Task Learning, Early Stopping, Sparse Representations and Optimization for Training Deep Models.
Module 3:
Convolutional Neural Networks, Convolution Operation, Pooling, Variants of Convolution, Structured Outputs, Data Types, Efficient Convolution Algorithms and History of CNNs.
Module 4:
Sequence Modelling, Unfolding Computational Graphs, Recurrent Neural Networks, Bidirectional RNNs, Encoder-Decoder Sequence-to-Sequence Architecture, Deep Recurrent Networks, Recursive Neural Networks and LSTM.
Module 5:
Interactive Applications of Deep Learning, NLP Preprocessing, word2vec, Word Embeddings, ROC-AUC, IMDb Film Review Classification, Dense Networks, CNNs for NLP, RNN, LSTM, Bidirectional LSTM, Stacked Recurrent Models, Seq2seq, Attention and Transfer Learning in NLP.
Features:
• Simple English and easy-to-understand explanations
• Point-wise exam-oriented answers
• Clean diagrams and important equations
• Quick revision points
• Important VTU questions
• High-priority exam topics
• Suitable for Internal Assessment and SEE preparation
Prepared by Search Creators.
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