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Building your own Neural Network from Scratch with Python

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Building your own Neural Network from Scratch with Python, available at $74.99, has an average rating of 4.1, with 51 lectures, based on 25 reviews, and has 2794 subscribers.

You will learn about Introductory Python, to more advanced concepts like Object Oriented Programming, decorators, generators, and even specialized libraries like Numpy & Matplotlib Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle. Linear Regression, Logistic Regression and Neural Networks built from scratch. Building a simple Deep Learning library from first principles This course is ideal for individuals who are Beginner Python Developers curious about Deep Learning. or Deep Learning Practitioners who want gain a mastery of how things work under the hood or Anyone who wants to master deep learning fundamentals. or Mastery of how Deep Learning libraries work and are built from scratch. It is particularly useful for Beginner Python Developers curious about Deep Learning. or Deep Learning Practitioners who want gain a mastery of how things work under the hood or Anyone who wants to master deep learning fundamentals. or Mastery of how Deep Learning libraries work and are built from scratch.

Enroll now: Building your own Neural Network from Scratch with Python

Summary

Title: Building your own Neural Network from Scratch with Python

Price: $74.99

Average Rating: 4.1

Number of Lectures: 51

Number of Published Lectures: 51

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Introductory Python, to more advanced concepts like Object Oriented Programming, decorators, generators, and even specialized libraries like Numpy & Matplotlib
  • Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.
  • Linear Regression, Logistic Regression and Neural Networks built from scratch.
  • Building a simple Deep Learning library from first principles

Who Should Attend

  • Beginner Python Developers curious about Deep Learning.
  • Deep Learning Practitioners who want gain a mastery of how things work under the hood
  • Anyone who wants to master deep learning fundamentals.
  • Mastery of how Deep Learning libraries work and are built from scratch.

Target Audiences

  • Beginner Python Developers curious about Deep Learning.
  • Deep Learning Practitioners who want gain a mastery of how things work under the hood
  • Anyone who wants to master deep learning fundamentals.
  • Mastery of how Deep Learning libraries work and are built from scratch.

Together we are going to master in depth concepts in machine learning and python programming, then apply our knowledge in building our own neural network from scratch without using any library.

What you’ll learn in this course will not only lay a solid foundation in your Deep Learning career, but also permit you to understand how deep learning libraries work.

If you’ve gotten to this point, it means you are interested in mastering how neural networks work and using your skills to solve practical problems.

You may already have some knowledge on Machine learning and python programming, or you may be coming in contact with these for the very first time. It doesn’t matter from which end you come from, because At the end of this course, you shall be an expert with much hands-on experience.

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!

This course is offered to you by Neuralearn.

And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum, will help us better this course.

Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Let’s get started.

Here are the different concepts you’ll master after completing this course.

  • Fundamentals Machine Learning.

  • Essential Python Programming

  • Choosing Machine Model based on task

  • Error sanctioning

  • Linear Regression

  • Logistic Regression

  • Multi-class Regression

  • Neural Networks

  • Training and optimization

  • Performance Measurement

  • Validation and Testing

  • Building Machine Learning models from scratch in python.

  • Overfitting and Underfitting

  • Shuffling

  • Ensembling

  • Weight initialization

  • Data imbalance

  • Learning rate decay

  • Normalization

  • Hyperparameter tuning

YOU’LL ALSO GET:

  • Lifetime access to This Course

  • Friendly and Prompt support in the Q&A section

  • Udemy Certificate of Completion available for download

  • 30-day money back guarantee

Who this course is for:

  • Beginner Python Developers curious about Deep Learning.

  • Deep Learning Practitioners who want gain a mastery of how things work under the hood.

  • Anyone who wants to master deep learning fundamentals.

  • Mastery of how Deep Learning libraries work and are built from scratch.

ENjoy!!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: General Introduction

Lecture 3: Why Build a Neural Network from Scratch?

Lecture 4: About this Course

Lecture 5: Link to Code

Chapter 2: Essential Python Programming

Lecture 1: Python Installation

Lecture 2: Variables and Basic Operators

Lecture 3: Conditional Statements

Lecture 4: Loops

Lecture 5: Methods

Lecture 6: Objects and Classes

Lecture 7: Operator Overloading

Lecture 8: Method Types

Lecture 9: Inheritance

Lecture 10: Encapsulation

Lecture 11: Polymorphism

Lecture 12: Decorators

Lecture 13: Generators

Lecture 14: Numpy Package

Lecture 15: Introduction to Matplotlib

Chapter 3: Introduction to Machine Learning

Lecture 1: Task – Machine Learning Development Life Cycle

Lecture 2: Data – Machine Learning Development Life Cycle

Lecture 3: Model – Machine Learning Development Life Cycle

Lecture 4: Error Sanctioning – Machine Learning Development Life Cycle

Lecture 5: Linear Regression

Lecture 6: Logistic Regression

Lecture 7: Linear Regression Practice

Lecture 8: Logistic Regression Practice

Lecture 9: Optimization

Lecture 10: Performance Measurement

Lecture 11: Validation and Testing

Chapter 4: Softmax Regression

Lecture 1: Data

Lecture 2: Modeling

Lecture 3: Error Sanctioning

Lecture 4: Training and Optimization

Lecture 5: Performance Measurement

Chapter 5: Neural Networks

Lecture 1: Modeling

Lecture 2: Error Sanctioning

Lecture 3: Training and Optimization

Lecture 4: Training and Optimization Practice

Lecture 5: Performance Measurement

Lecture 6: Validation and Testing

Lecture 7: Solving Overfitting and Underfitting

Lecture 8: Shuffling

Lecture 9: Ensembling

Lecture 10: Weight Initialization

Lecture 11: Data Imbalance

Lecture 12: Learning rate decay

Lecture 13: Normalization

Lecture 14: Hyperparameter tuning

Lecture 15: In Class Exercise

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 2 votes
  • 3 stars: 5 votes
  • 4 stars: 4 votes
  • 5 stars: 14 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!

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