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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.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Asst.prof M.Gokilavani
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.
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
Asst.prof M.Gokilavani
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.
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
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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 IV QUESTION BANK.docx.pdf
GE3151 PSPP UNIT IV QUESTION BANK.docx.pdf
GE3151 PSPP UNIT IV QUESTION BANK.docx.pdf
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GE3151 PSPP UNIT III QUESTION BANK.docx.pdf
GE3151 PSPP UNIT III QUESTION BANK.docx.pdf
GE3151 PSPP UNIT III QUESTION BANK.docx.pdf
Asst.prof M.Gokilavani
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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.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Asst.prof M.Gokilavani
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.
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
Asst.prof M.Gokilavani
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.
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
Asst.prof M.Gokilavani
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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CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
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IT8073_Information Security_UNIT I _.pdf
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Rule 1 − Check for the blocks connected in series and simplify. Rule 2 − Check for the blocks connected in parallel and simplify. Rule 3 − Check for the blocks connected in feedback loop and simplify. Rule 4 − If there is difficulty with take-off point while simplifying, shift it towards right. Rule 5 − If there is difficulty with summing point while simplifying, shift it towards left. Rule 6 − Repeat the above steps till you get the simplified form, i.e., single block.
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Block diagram reduction techniques in control systems.ppt
Block diagram reduction techniques in control systems.ppt
UNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its Performance
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
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