Blog posts and tutorials for learning the nuances of DL.
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https://medium.com/@jonathan_hui/deep-learning-designs-part-3-e0b15ef09ccc
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https://machinelearningmastery.com/visualize-deep-learning-neural-network-model-keras/
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To get reproducible results every time you run your network, initialize seeds of random generators. Use the following.
import numpy as np
np.random.seed(7)
import random
random.seed(7)
from tensorflow import set_random_seed
set_random_seed(7)
Also, initialize weights of conv layers without randomness. Example, use 'glorot_uniform' which uses fan_in and fan_out as factors.
In PyTorch, use
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(999)
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Use ProgramCreek for example codes. Example https://www.programcreek.com/python/example/107694/torch.nn.LSTM
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For RNN-LSTM learning use Andrew Ng's lectures at https://www.youtube.com/playlist?list=PL1w8k37X_6L_s4ncq-swTBvKDWnRSrinI and Christopher Olah's blog http://colah.github.io/posts/2015-08-Understanding-LSTMs/
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An intuitive explanation of cross-entropy loss https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a
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L0,L1,L2 and L-infinity norms: https://medium.com/@montjoile/l0-norm-l1-norm-l2-norm-l-infinity-norm-7a7d18a4f40c and https://rorasa.wordpress.com/2012/05/13/l0-norm-l1-norm-l2-norm-l-infinity-norm/
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A bit of history about phonetically balanced sentences: https://gizmodo.com/the-harvard-sentences-secretly-shaped-the-development-1689793568 [Not exactly related to DL but audio engineering.]
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Data Visualization: https://medium.com/swlh/effective-visualization-of-multi-dimensional-data-a-hands-on-approach-b48f36a56ee8
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Bias-Variance Trade Off: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff
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Variational Auto Encoder: https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73