Aditya K 97k

🛠️

Building AI-Tools

Software Developer who has his way of building things and automating stuff

97k / backpropagation.py

Last active April 12, 2019 08:48

Backpropagation code for deep neural net

	def backprop_this_layer(self, da_curr, z_curr, W_curr, b_curr, A_prev, activation_function):
	if activation_function is 'sigmoid':
	activation_back = self.sigmoid_backward
	elif activation_function is 'relu':
	activation_back = self.relu_backward
	else:
	return
	m = A_prev.shape[1]

	dz_curr = activation_back(da_curr, z_curr)

97k / ForwardPass.py

Created March 19, 2019 12:04

Forward Propagation

	def forward_prop_this_layer(self, A_prev, W_curr, b_curr, activation_function):
	z_curr = np.dot(W_curr, A_prev) + b_curr

	if activation_function is 'relu':
	activation = relu
	elif activation_function is 'sigmoid':
	activation = sigmoid
	else:
	raise Exception(f"{activation_function} is currently not supported, Only sigmoid, relu are supported")

97k / initialize_params.py

Created March 18, 2019 09:34

	def initialize_params(self, architecture):
	# We'll save parameters in a dictionary so that, we can acess them later on
	params = {}
	for id_, layer in enumerate(architecture):
	# We're starting our layer from 1, There's a reason for this, think about it.
	layer_id = id_ + 1
	# With help of architecture provided, we'll get dimensions for each layer.
	input_dim = layer['input_dim']
	output_dim = layer['output_dim']

97k / architecture.py

Last active March 18, 2019 09:29

neural network architecture

	NN_ARCHITECTURE = [
	{"input_dim": 3, "output_dim": 4, "activation": "relu"},# First Hidden Layer
	{"input_dim": 4, "output_dim": 4, "activation": "relu"},# Second Hidden Layer
	{"input_dim": 4, "output_dim": 1, "activation": "sigmoid"},# Output Layer
	]

97k / Kmeans.py

Created July 12, 2018 11:32

Sample Code which represent Kmeans algo and plots the result using matplotlib. Dataset was made using make_blobs(sklearn.dataset.make_blobs)

	import matplotlib.pyplot as plt
	import seaborn as sns

	from sklearn.datasets import make_blobs
	# data is a tuple with 2 columns, one is the dataset that we made, and second is the labels representing centre.
	data = make_blobs(n_samples = 200, n_features=2, centers=3, cluster_std=1.5)

	from sklearn.cluster import KMeans
	kmeans = KMeans(n_clusters=3)
	kmeans.fit(data[0])

97k / Codeheat.md

Created February 1, 2018 18:37

CodeHeat 2017-18 | Final Gist

CodeHeat 2017-18

I have worked on following projects in this contest :