# Neural Networks in Python from Scratch: Complete guide

## Neural Networks in Python from Scratch: Complete guide

**Neural Networks** in **Python** from Scratch: Complete guide Learn the fundamentals of Deep Learning of **neural networks** in **Python** both in theory and practice!

### What you'll learn

- Learn step by step all the mathematical calculations involving artificial neural networks
- Implement neural networks in Python and
**Numpy**from scratch - Understand concepts like
**perceptron**, activation functions,**backpropagation**, gradient descent, learning rate, and others - Build neural networks applied to classification and regression tasks
- Implement neural networks using libraries, such as:
**Pybrain**,**sklearn**, TensorFlow, and PyTorch

#### Requirements

- Programming logic (if, while and for statements)
- Basic Python programming
- No prior knowledge about Artificial Neural Networks or Artificial Intelligence

### Description

Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis.

One of the biggest problems that
I’ve seen in students that start learning about neural networks is the
lack of easily understandable content. This is due to the fact that the
majority of the materials that are available are very technical and
apply a lot of mathematical formulas, which simply makes the learning
process incredibly difficult for whomever wishes to take their first
steps in this field. With this in mind, the main objective of this
course is to present the theoretical and mathematical concepts of neural
networks in a simple yet thorough way, so even if you know nothing
about neural networks, you’ll understand all the processes. We’ll cover
concepts such as **perceptrons**, activation functions, **multilayer** networks,
gradient descent and **backpropagation** algorithms, which form the
foundations through which you will understand fully how a neural network
is made. We’ll also cover the implementations on a step-by-step basis
using Python, which is one of the most popular programming languages in
the field of Data Science. It’s important to highlight that the
step-by-step implementations will be done without using Machine
Learning-specific Python libraries, because the idea behind this course
is for you to understand how to do all the calculations necessary in
order to build a neural network from scratch.

To sum it all up, if you wish to take your first steps in Deep Learning, this course will give you everything you need. It’s also important to note that this course is for students who are getting started with neural networks, therefore the explanations will deliberately be slow and cover each step thoroughly in order for you to learn the content in the best way possible. On the other hand, if you already know your way around neural networks, this course will be very useful for you to revise and review some important concepts.