Update DLwP link

Author: fchollet

guides/ipynb/transfer_learning.ipynb

@@ -77,7 +77,7 @@
     " dataset.\n",
     "\n",
     "This is adapted from\n",
-    "[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)\n",
+    "[Deep Learning with Python](https://deeplearningwithpython.io/)\n",
     "and the 2016 blog post\n",
     "[\"building powerful image classification models using very little data\"](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)."
    ]

guides/ipynb/writing_a_custom_training_loop_in_tensorflow.ipynb

@@ -587,7 +587,7 @@
     "as real.\n",
     "\n",
     "For a much more detailed overview of how GANs works, see\n",
-    "[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python).\n",
+    "[Deep Learning with Python](https://deeplearningwithpython.io/).\n",
     "\n",
     "Let's implement this training loop. First, create the discriminator meant to classify\n",
     "fake vs real digits:"

guides/ipynb/writing_a_training_loop_from_scratch.ipynb

@@ -579,7 +579,7 @@
     "as real.\n",
     "\n",
     "For a much more detailed overview of how GANs works, see\n",
-    "[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python).\n",
+    "[Deep Learning with Python](https://deeplearningwithpython.io/).\n",
     "\n",
     "Let's implement this training loop. First, create the discriminator meant to classify\n",
     "fake vs real digits:"

guides/md/transfer_learning.md

@@ -56,7 +56,7 @@ ImageNet dataset, and retraining it on the Kaggle "cats vs dogs" classification
  dataset.
 
 This is adapted from
-[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)
+[Deep Learning with Python](https://deeplearningwithpython.io/)
 and the 2016 blog post
 ["building powerful image classification models using very little data"](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html).
 

guides/md/writing_a_custom_training_loop_in_tensorflow.md

@@ -678,7 +678,7 @@ A GAN training loop looks like this:
 as real.
 
 For a much more detailed overview of how GANs works, see
-[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python).
+[Deep Learning with Python](https://deeplearningwithpython.io/).
 
 Let's implement this training loop. First, create the discriminator meant to classify
 fake vs real digits:

guides/md/writing_a_training_loop_from_scratch.md

@@ -499,7 +499,7 @@ A GAN training loop looks like this:
 as real.
 
 For a much more detailed overview of how GANs works, see
-[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python).
+[Deep Learning with Python](https://deeplearningwithpython.io/).
 
 Let's implement this training loop. First, create the discriminator meant to classify
 fake vs real digits:

guides/transfer_learning.py

@@ -51,7 +51,7 @@
  dataset.
 
 This is adapted from
-[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)
+[Deep Learning with Python](https://deeplearningwithpython.io/)
 and the 2016 blog post
 ["building powerful image classification models using very little data"](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html).
 """

guides/writing_a_custom_training_loop_in_tensorflow.py

@@ -382,7 +382,7 @@ def train_step(x, y):
 as real.
 
 For a much more detailed overview of how GANs works, see
-[Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python).
+[Deep Learning with Python](https://deeplearningwithpython.io/).
 
 Let's implement this training loop. First, create the discriminator meant to classify
 fake vs real digits:

templates/getting_started/faq.md

@@ -672,8 +672,8 @@ See the following guides:
 
 ### What's the difference between `Model` methods `predict()` and `__call__()`?
 
-Let's answer with an extract from
-[Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras):
+Let's answer with an extract from the book
+[Deep Learning with Python](https://deeplearningwithpython.io/):
 
 > Both `y = model.predict(x)` and `y = model(x)` (where `x` is an array of input data)
 > mean "run the model on `x` and retrieve the output `y`." Yet they aren't exactly