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*Total -- 36,238.47kb -> 29,458.58kb (18.71%)

/Chapter-wise code/Code - PyTorch/3. Recurrent Neural Networks/images/outliers_padding_ex.png -- 25.24kb -> 9.11kb (63.89%)
/Chapter-wise code/Code - PyTorch/2. Convolution Neural Networks/8. Autoencoders/images/learning_autoencoder.png -- 12.69kb -> 5.56kb (56.16%)
/Chapter-wise code/Code - PyTorch/2. Convolution Neural Networks/8. Autoencoders/images/sparse_encoders.png -- 7.91kb -> 3.48kb (55.94%)
/Chapter-wise code/Code - PyTorch/3. Recurrent Neural Networks/1. Text generation using RNNs/assets/charRNN.png -- 32.69kb -> 18.47kb (43.48%)
/Chapter-wise code/Code - PyTorch/4. Generative Adversarial Networks (GANs)/1. Generating hand-written digits using GANs/images/tanh_fn.png -- 216.79kb -> 126.69kb (41.56%)
/Chapter-wise code/Code - PyTorch/3. Recurrent Neural Networks/1. Text generation using RNNs/assets/sequence_batching@1x.png -- 55.89kb -> 32.68kb (41.53%)
/Chapter-wise code/Code - PyTorch/4. Generative Adversarial Networks (GANs)/1. Generating hand-written digits using GANs/images/leaky_relu.png -- 239.64kb -> 142.08kb (40.71%)
/Chapter-wise notes/Ch_1_Linear_algebra/images/eigen_decompost.png -- 4.23kb -> 2.63kb (37.8%)
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/Chapter-wise code/Code - PyTorch/2. Convolution Neural Networks/8. Autoencoders/images/denoising.png -- 34.70kb -> 21.73kb (37.36%)
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/Chapter-wise code/Code - PyTorch/6. Natural-Language-Processing/4. Topic Modelling/images/18. sample a word - 3.png -- 459.65kb -> 332.78kb (27.6%)
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/Chapter-wise code/Code - PyTorch/2. Convolution Neural Networks/8. Autoencoders/images/autoencoder_denoise.png -- 135.98kb -> 98.78kb (27.36%)
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/Chapter-wise code/Code - PyTorch/3. Recurrent Neural Networks/1. Text generation using RNNs/assets/charRNN@0.5x.png -- 15.81kb -> 11.55kb (26.94%)
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