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CS304PC:Computer Organization and Architecture Unit III Problems Solution .pdf
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LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.
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UNIT III THIRD-GENERATION NEURAL NETWORKS Spiking Neural Networks-Convolutional Neural Networks-Deep Learning Neural Networks-Extreme Learning Machine Model-Convolutional Neural Networks: The Convolution Operation – Motivation –Pooling – Variants of the basic Convolution Function – Structured Outputs – Data Types – Efficient Convolution Algorithms – Neuroscientific Basis –Applications: Computer Vision, Image Generation, Image Compression.
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UNIT II ASSOCIATIVE MEMORY AND UNSUPERVISED LEARNING NETWORKS Training Algorithms for Pattern Association-Autoassociative Memory Network-Heteroassociative Memory Network-Bidirectional Associative Memory (BAM)-Hopfield Networks-Iterative Autoassociative Memory Networks-Temporal Associative Memory Network-Fixed Weight Competitive Nets-Kohonen Self-Organizing Feature Maps-Learning Vector Quantization-Counter propagation Networks-Adaptive Resonance Theory Network.
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UNIT I INTRODUCTION Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of ANNs-Supervised Learning Network.
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GE3151 PSPP UNIT III QUESTION BANK.docx.pdf
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LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.
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UNIT III THIRD-GENERATION NEURAL NETWORKS Spiking Neural Networks-Convolutional Neural Networks-Deep Learning Neural Networks-Extreme Learning Machine Model-Convolutional Neural Networks: The Convolution Operation – Motivation –Pooling – Variants of the basic Convolution Function – Structured Outputs – Data Types – Efficient Convolution Algorithms – Neuroscientific Basis –Applications: Computer Vision, Image Generation, Image Compression.
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CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
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UNIT II ASSOCIATIVE MEMORY AND UNSUPERVISED LEARNING NETWORKS Training Algorithms for Pattern Association-Autoassociative Memory Network-Heteroassociative Memory Network-Bidirectional Associative Memory (BAM)-Hopfield Networks-Iterative Autoassociative Memory Networks-Temporal Associative Memory Network-Fixed Weight Competitive Nets-Kohonen Self-Organizing Feature Maps-Learning Vector Quantization-Counter propagation Networks-Adaptive Resonance Theory Network.
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