Descriptions for Deep learning experiments

Desciptions only:



 1.       Image Classification


For Image classification binary, CNN will have one nueron at output layer. In this experiment  of Image classification,  sample images of dogs are trained , tested and prediction using a basic CNN Network.






2.      Actor Age groups



For Image classification and labeling the images into Young, Middle, and Old in this experiment, A CNN of three layers used and by inputting training data with labeling.



3. Hyper parameted tuning


Hyper parameters are the parameters given in the program. Tuning involves modifying parameters to try perforamce


4. Sequential data prediction


Training Sequential data prediction models from various days .



5. Image denoising


Training Image noinse models to denoise roman numbers 


6. YOLO     


YOLO prgromas are used to detect objects. 


7. Binary logistic loss

Optimization purpose


8.  Alexnet 


CIFAR Dataset and Classifcation of CIFAR -10, 10 Classsifications ( Multi class classifcation)


9. Autoencoders


Anomoly detection . Anamoly detection carried out by the process of training Normal data, Anamoly data and pooling. Encoders applied for pattern recognition. 


10 GAN


Image generation by implicit training




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