Coding A Neural Network FROM SCRATCH! (Part 2)

[FlatterBaker]

I smell an underrated channel.
You are literally the savior of my science fair project, thank you so much.

[willysmb-bovn]

Hey! I'm wondering when Part 3 is coming out. Can't wait to see it!

[DarthAnimal]

The Brain function can be heavily simplified

You can put the two edge cases outside of the loop, caling layers[0], and layers[layers.length-1], and having the for loop start with i=1, and run while 1 < layers.length -1

[PatrykPonichtera]

I've seen a lot of videos about Neural Networks and yours is the one that explains it in an understandable manner (Or maybe the 10th time is the charm)
I'm curious to see the next one

[drewdowsett]

A great series. Not only for content, but well edited too. Cheers John.

[mehmatrix]

Great video. Thanks for the effort. I'm looking forward to see the part 3. Cheers,

[voil]

I was following along in python. Here's the code if anyone wants it. I didn't test it though because I don't really know how to use it. Tutorial was too short ):

networkShape = [2, 4, 4, 2]

class Layer(object):
def __init__(self, n_inputs, n_nodes):
self.n_nodes = n_nodes
self.n_inputs = n_inputs

self.weightsArray = [n_nodes, n_inputs]
self.biasesArray = [n_nodes]
self.nodeArray = [n_nodes]

def forward(self, inputsArray):
self.nodeArray = [self.n_nodes]

for i in range(self.n_nodes):
# Sum of the weights times inputs
for j in range(self.n_inputs):
self.nodeArray[i] += self.weightsArray[i, j] * inputsArray
# Add the bias
self.nodesArray[i] += self.biasesArray[i]

def activation(self):
for i in range(self.n_nodes):
if self.nodeArray[i] < 0:
self.nodeArray[i] = 0

def awake():
global layers

# layers = Layer(len(networkShape) - 1)
layers = []
for i in range(len(networkShape) - 1):
# layers[i] = Layer(networkShape[i], networkShape[i + 1])
layers.append(Layer(networkShape[i], networkShape[i + 1]))

def brain(inputs):
for i in range(len(layers)):
if i == 0:
layers[i].forward(inputs)
layers[i].activation()
elif i == len(layers) - 1:
layers[i].forward(layers[i - 1].nodeArray)
else:
layers[i].forward(layers[i - 1].nodeArray)
layers[i].activation()

return layers[-1].nodeArray

[patricksturgill]

Hopefully you'll finish this eventually! I enjoyed the last two videos.

[slarcraft]

Nice video! In general I'd say a neural network is still a black box even if you built it and know the values of all the nodes, weights, biases and layers.

[bencebob]

thank you so much! this is exactly what i need for my uni project

[wyrdokward]

Great video ! I'm really looking forward to see how the network will be trained

[DanielYong-ok]

isn't the 'layer' in layer[ i ] = new Layer( networkShape[ i ], networkShape[ i + 1 ] ); supposed to be 'layers' ?

[gustavosalmeron]

That was an excellent, practical video on neural networks! As someone just beginning to dig into this subject, I love it!

Also, clean and neat code. Enjoyable to read (although I'm not a fan of nesting classes).

[Bloodbone]

Hey! I wonder when episode 3 will come out?

[t.p.]

best video ive ever seen not gonna lie

[TheZazatv]

O man thank you! Such a gem content out here :) I'm a Swift Dev the code is not hard to grasp

[erinleighlynch]

:') Episode 3 where are you... this is the new Half Life 3 for me. Am I wrong in thinking the weights and biases were never given values here? should those be made in this class too?

[aesvarash]

I wonder that the shape of network . I mean how many hidden layers and nodes should we use for each sample .
And also wondering about third part .

[thimodemoura]

i need that next video i have no idea what im doing :(

i have this code and i think i understood how it works after starring at it for an Eternity BUT how can i make use of it now....

[adirmugrabi]

if the activation function always happen right after forward. why not just combine them?