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Author: imteekay

content/building-a-deep-neural-network-from-scratch/en/index.mdx

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-In the [last post](https://www.iamtk.co/building-a-neural-network-from-scratch-with-mathematics-and-python), I wrote about how a neural network works under the hood, from math to code, from theory to Python. The post talked about how to build a simple neural net, also called a shallow neural network because it has only one hidden layer (together with the input and the output layers).
+In the [last post](/building-a-neural-network-from-scratch-with-mathematics-and-python), I wrote about how a neural network works under the hood, from math to code, from theory to Python. The post talked about how to build a simple neural net, also called a shallow neural network because it has only one hidden layer (together with the input and the output layers).
 
 In this post, I plan to build upon that first implementation of a shallow neural net and transform it into a deep neural net. The first idea is to just increase the neural net in one layer to see how it behaves, and then abstract it into a reusable class, and make it easy to pass the number of layers and hidden units per layer and the model figures out how to build on top of that.
 
 Adding more layers and hidden units to the neural net increases the complexity of the model and so we need to understand other topics like input normalization, vanishing and exploding gradients, regularization, mini-batches, and other interesting concepts so we can make the deep neural net perform better on the dataset problem.
 
 ## Adding one layer
 
-To revisit the previous implementation of the shallow neural network, take a look at the previous [blog post](https://www.iamtk.co/building-a-neural-network-from-scratch-with-mathematics-and-python) and the [neural net implementation on github](https://github.com/imteekay/neural-network-from-scratch). This first part and most of this post will work on top of that implementation with a few tweaks and changes.
+To revisit the previous implementation of the shallow neural network, take a look at the previous [blog post](/building-a-neural-network-from-scratch-with-mathematics-and-python) and the [neural net implementation on github](https://github.com/imteekay/neural-network-from-scratch). This first part and most of this post will work on top of that implementation with a few tweaks and changes.
 
 To add a new hidden layer, we need a few changes in the code:
 
@@ -689,6 +689,6 @@ Until then.
 
 - [Mathematics for Machine Learning](/series/mathematics-for-machine-learning)
 - [Machine Learning Research](https://github.com/imteekay/machine-learning-research)
-- [Building A Neural Network from Scratch with Mathematics and Python](https://www.iamtk.co/building-a-neural-network-from-scratch-with-mathematics-and-python)
+- [Building A Neural Network from Scratch with Mathematics and Python](/building-a-neural-network-from-scratch-with-mathematics-and-python)
 
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content/building-a-neural-network-from-scratch-with-mathematics-and-python/en/index.mdx

@@ -544,6 +544,8 @@ B2 = np.zeros((10, 1))
 
 In a future post, I want to expand this algorithm and add more layers so we can test if the neural network learns and performs better. In other words, deep neural networks would perform better than shallow neural nets? Let's see in a future experiment.
 
+**Edit**: I published the new post with the implementation of the deep neural network with multiple layers: [Building A Deep Neural Network from Scratch](/building-a-deep-neural-network-from-scratch)
+
 ## Resources
 
 - [Mathematics for Machine Learning](/series/mathematics-for-machine-learning)