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View MathematicaLSTM.nb
(* Content-type: application/vnd.wolfram.mathematica *)
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(* CreatedBy='Mathematica 11.3' *)
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View TensorFlow hello world.md
	import tensorflow as tf


	import numpy as np
	x_input = np.array([[1,2,3,4,5]])
	y_input = np.array([[10]])
View Pytorch hello world.md
	import torch


	batch_size = 32
	input_shape = 5
	output_shape = 10
View Keras hello world.md

Using Keras (now part of TensorFlow) is really easy. The complexity comes when you deal with large amounts of data figuring out the topology of a neural network. With the topology comes hyperparameter tuning and all that. It's a bit like painting: it's easy to hold a brush but it takes years to paint something worth looking at.

	import tensorflow as tf
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense
View Gluon hello world.md
	from mxnet import gluon

	import mxnet as mx
	import numpy as np
	x_input = mx.nd.empty((1, 5), mx.cpu())
	x_input[:] = np.array([[1,2,3,4,5]], np.float32)

	y_input = mx.nd.empty((1, 5), mx.cpu())
	y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32)
View TFjsCosine.html
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View PyroBasic.ipynb
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View PPLIris.ipynb
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View MxNetLinearRegression.ipynb
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View KerasUnitLearner.py
from keras.layers.core import Dense
from keras.models import Sequential
from numpy import array
import numpy as np
import os
import argparse
from scipy import signal
N = 10