Grigory Sapunov (10) [Avatar] Offline
In this new coordinate system, the coordinates of our points can be said to be a new
"representation" of our data. And it’s a good one! With this representation, the
black/white classification problem can be expressed as a simple rule: black points are
such that x 0 or "white points are such that x < 0". Our new representation basically
solves the classification problem.

Should be:
black points are such that x > 0

All machine learning algorithms consist of automatically finding such
transformations that turn data into more useful representations for a given task. These
operations could sometimes be coordinate changes, as we just saw, or could be linear
projections (which may destroy information), translations, non-linear operations (such as
select all points such that x 0), etc.

Should be:
select all points such that x > 0
Grigory Sapunov (10) [Avatar] Offline
The deep learning hardware
startup Nervana Systems was acquired by Intel in 2016 for over $400.

Should be:
for over $400M
Grigory Sapunov (10) [Avatar] Offline
By the end of this chapter, you will already be able to use neural networks to solve
simple machine problems such as classification or regression over vector data.

Should be:
simple machine learning problems such as classification or regression over vector data.
Grigory Sapunov (10) [Avatar] Offline
Like hold-out validation, this method doesn’t exempt you from using a distinct validation set for model calibration.

Do we really need a distinct validation set here?

Grigory Sapunov (10) [Avatar] Offline
Listing 5.9
# Directory with our validation cat pictures
test_cats_dir = os.path.join(test_dir, 'cats')
# Directory with our validation dog pictures
test_dogs_dir = os.path.join(test_dir, 'dogs')

Should be:
# Directory with our test cat pictures
# Directory with our test dog pictures
Grigory Sapunov (10) [Avatar] Offline

In Listing 6.2 there should be a limit up to maxlen symbols something like in Listing 6.1:

for i, sample in enumerate(samples):
for j, character in list(enumerate(sample))[:max_length]:
index = token_index.get(character)
results[i, j, index] = 1.
Grigory Sapunov (10) [Avatar] Offline

This error tells you, in essence, that Keras was not able to reach input_1 from the provided output tensor.

Is it correct? It looks like Keras was not able to reach input_1 from the provided input tensor, not the output one.
Grigory Sapunov (10) [Avatar] Offline
VGG19 is a simple variant of the VGG16 network we introduced in Chapter 5, with three more convolutional layers

It looks strange that the more complex network is called as a simple variant. I understood it as the VGG19 is simpler than VGG16.
Grigory Sapunov (10) [Avatar] Offline
Listing 8.31
# Produce a 32x32 1-channel feature map
x = layers.Conv2D(channels, 7, activation='tanh', padding='same')(x)

Should be:
# Produce a 32x32 3-channel feature map
Grigory Sapunov (10) [Avatar] Offline
Figure 8.18 Play the discriminator: in each row, two images were dreamed up by our GAN,
and one image comes from the training set. Can you tell them apart? (answers - real
images: middle, top, bottom, middle)

Should be:
In each column
svenproppert (5) [Avatar] Offline
Figure 6.28 is the same as figure 6.26 although indicated otherwise.