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`@@ -29,11 +29,22 @@ Subjcts :`
`29`	`29`	`- Cyclical Annealing Schedule`
`30`	`30`	`- [x] MultiTask Learning`
`31`	`31`	`- [ ] GANs (GAN, DCGAN, CGAN, CycleGAN, StarGAN, StyleGAN, WGAN, etc)`
`32`		`-- [ ] RNNs(RNN, LSTM, GRU)`
	`32`	`+- [ ] RNNs(RNN, LSTM, GRU) (NLP and Vision)`
	`33`	`+ - [ ] Text Generation`
	`34`	`+ - [ ] Sentiment Analysis`
	`35`	`+ - [ ] Seq2Seq`
	`36`	`+ - [ ] Attention Mechanism`
	`37`	`+ - [ ] Transformers`
	`38`	`+ - [ ] Image Captioning`
	`39`	`+ - [ ] CTC Loss`
	`40`	`+ - [ ] Word Embedding`
	`41`	`+ - [ ] NER(Named Entity Recognition)`
	`42`	`+ - [ ] Misc`
`33`	`43`	`- [ ] Style transfer`
`34`	`44`	`- [ ] Adversarial Attacks (Examples)`
`35`	`45`	`- [ ] Object Detection`
`36`	`46`	`- [ ] Semantic Segmentation`
	`47`	`+- [ ] Siamese Networks`
`37`	`48`	`- [ ] Autograd introduction`
`38`	`49`	`- [ ] Datasets Introduction`
`39`	`50`	`- Misc`

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