Neural Networks In Python From Scratch. Build step by step!

Neural Networks In Python From Scratch. Build step by step!, available at $54.99, has an average rating of 4.62, with 24 lectures, based on 302 reviews, and has 1510 subscribers.

You will learn about The basic functions for any neural network, by coding linear regression, cost functions and back propagation Understand the properties of neural networks by adjusting learning rates and biases Train a network by implementing a gradient descent algorithm Normalizing inputs for multi-input networks Create classification networks by implementing multiple output neurons and activation Improve network accuracy by implementing hidden layers for non-linear data This course is ideal for individuals who are Developer who want to learn the mechanics of neural networks or Developers who want to avoid using neural network libraries and frameworks or Developers who use frameworks but want to learn the meaning of the individual network parameters It is particularly useful for Developer who want to learn the mechanics of neural networks or Developers who want to avoid using neural network libraries and frameworks or Developers who use frameworks but want to learn the meaning of the individual network parameters.

Enroll now: Neural Networks In Python From Scratch. Build step by step!

Summary

Title: Neural Networks In Python From Scratch. Build step by step!

Price: $54.99

Average Rating: 4.62

Number of Lectures: 24

Number of Published Lectures: 24

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

The basic functions for any neural network, by coding linear regression, cost functions and back propagation

Understand the properties of neural networks by adjusting learning rates and biases

Train a network by implementing a gradient descent algorithm

Normalizing inputs for multi-input networks

Create classification networks by implementing multiple output neurons and activation

Improve network accuracy by implementing hidden layers for non-linear data

Who Should Attend

Developer who want to learn the mechanics of neural networks

Developers who want to avoid using neural network libraries and frameworks

Developers who use frameworks but want to learn the meaning of the individual network parameters

Target Audiences

Developer who want to learn the mechanics of neural networks

Developers who want to avoid using neural network libraries and frameworks

Developers who use frameworks but want to learn the meaning of the individual network parameters

You will learn how to build Neural Networks with Python. Without the need for any library, you will see how a simple neural network from 4 lines of code, evolves into a artificial intelligence network that is able to recognize handwritten digits.

During this process, you will learn concepts like: Feed forward, Cost functions, Back propagation, Hidden layers, Linear regression, Gradient descent and Matrix multiplication. And all this with plain Python.

Target audience

Developers who especially benefit from this course, are:

Developer who want to learn the mechanics of neural networks

Developers who want to avoid using neural network libraries and frameworks

Or developers who use frameworks but want to learn the meaning of the individual network parameters

Challenges

Many tutorials claim to start from scratch, but import external libraries or rapidly type in code and before executing even once, you are looking at 50 lines of code. When finally the code is run, you are totally lost and still stuck trying to understand line 3.

This causes many students to give up learning Neural Networks.
This course is different! It starts with the absolute beginning and each topic is a continuation of a previous example. This way, you will learn neural networks from the ground up, step by step.

What can you do after this course?

You understand neural network concepts and ideas, like back propagation and gradient descent.

You are able to build a neural network in any programming language of choice, without the help of frameworks and libraries.

You understand how to better configure the network by plugging in different cost functions and adding hidden layers.

Topics

Linear regression

Cost functions

Bias

Multiple inputs

Normalisation

Gradient descent

Classification

Activation

Multi-class classification

Non-linear data

Hidden layers

Duration
3 hour video time. This course has no exercises.

The teacher
This course is taught by Loek van den Ouweland, a senior software engineer with 25 years of professional experience. Loek is the creator of Wunderlist for windows, Microsoft To-do and Mahjong for Windows and loves to teach software engineering.

Students of this course tell me:
* * * * * “Great, simple explanations. Perfect for beginners that have little pre knowledge of the topic.”
* * * * * “Straight to the point starting with the foundations.”
* * * * * “Clearly explained step by step how Neural Networks work and can be developed in a pure development language of choice without the usage of any external package..”

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: What can you expect from this course?

Lecture 2: Who are you and what do you need?

Lecture 3: MacOS: Install Python and Visual Studio Code (2024)

Lecture 4: Windows: Install Python and Visual Studio Code (2024)

Chapter 2: Neural Network Introduction

Lecture 1: What is a neural network?

Chapter 3: Linear Regression

Lecture 1: Linear Regression

Lecture 2: Cost functions

Lecture 3: Bias

Chapter 4: Real Data

Lecture 1: Real Data

Lecture 2: Second Input

Lecture 3: Normalise Data

Chapter 5: Classification

Lecture 1: Gradient Descent

Lecture 2: Classification Introduction

Lecture 3: Activation

Chapter 6: Multiclass Classification

Lecture 1: Softmax

Lecture 2: Non Linear Data

Chapter 7: Hidden Layers, Random Weights

Lecture 1: Adding Hidden Layers

Lecture 2: Recap

Lecture 3: Random Weights

Chapter 8: Handwritten Digits Recognition

Lecture 1: Mini Batch Gradient Descent

Lecture 2: Recognizing Handwritten Digits

Lecture 3: Q&A #1: What does [t – p for t, p in zip(targets, predictions)] do?

Lecture 4: Conclusion

Lecture 5: Bonus Chapter

Instructors

Loek van den Ouweland
Passionate Python Teacher

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 14 votes

4 stars: 57 votes

5 stars: 228 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!