Update README.md

Author: coderop2

README.md

@@ -7,8 +7,9 @@ Following projects can be found in this repository:-
 2. **CNN with/out Augmentation** [LINK](https://github.com/coderop2/Deep_Learning_projects/blob/main/CNN_with_out_augmentation.ipynb) - Although we are in the age of data where we can get data very readily on any topic or area that we desire, the problem is the amount of data we have. While for some cases we have so much data that the model might not be able to handle it, while in other we have so little that the model is not able to generalize well and ends up getting over fitting to the data thats present. So in order to avoid that and understand the use of augmentation i studied the effect of use of augmentation and not augmentation of the CIFAR 10 dataset with the use of CNNs and the network. Experimentation done :-
     - Augmentation Vs Non-augmented dataset - Augmentation won with over 3% better results, provided i was only using 3 augmentation techniques.
     - Transfer learning on a smaller subset of dataset from CIFAR and then further studing the effects of augmentation
-3. **RNN on Audio Data** [LINK]() - A simple RNN to leverage the use to sequential data in the audio file and train them on bidirectional LSTMs, that is after converting them into frequency and time domain which actually brings out the wave form of the audio files.
+3. **RNN on Audio Data** [LINK](https://github.com/coderop2/Deep_Learning_projects/blob/main/RNN_on_Audio.ipynb) - A simple RNN to leverage the use to sequential data in the audio file and train them on bidirectional LSTMs, that is after converting them into frequency and time domain which actually brings out the wave form of the audio files.
 4. **CNN on Audio Data** [LINK](https://github.com/coderop2/Deep_Learning_projects/blob/main/CNN_on_Audio.ipynb) - Although there might be a debate as too which might bring the most of audio files CNNs or RNNs. I simply ignore these debates and focus on FCNs and CNNs. Experiments done :-
     - Training and comparing audio files between a 5 layer fully connected neural network and a 5 layer deep CNN. The comparison was done using SNR for before and after. Althouh not surprising CNNs performed better than FCNs as they were able accomodate better the different peks and valleys of the signal than a FCN.
     - Further use a CNN to predict the secind half of the audio file given the first half (something like a audio GAN).
-5. 
+5. **Pixel Generation using RNN** [LINK](https://github.com/coderop2/Deep_Learning_projects/blob/main/Pixel_Gen_RNNs.ipynb) - A RNN is good at learning about sequential data and taking this fact into consideration i establish a goal to generate images based on the fact that some part is given(like the input is the top half and the job of the model isto predict the bottom half). So that the resulting image is a complete image. Data being used here is MNIST and getting a great accuracy of 98%-99% when compared to original 
+6. **CNN with SVD and Siamese Network** [LINK](https://github.com/coderop2/Deep_Learning_projects/blob/main/CNN_SVD_Siamese_Ntw.ipynb) -