Building your own Neural Network from Scratch with Python

Building your own Neural Network from Scratch with Python

Building your own Neural Network from Scratch with Python

Master how Machine Learning and Deep Learning algorithms and libraries work under the hood with practical examples.

Language: english

Note: 4.2/5 (9 notes) 2,419 students

Instructor(s): Neuralearn Dot AI

Last update: 2022-03-10

What you’ll 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

 

Requirements

  • Basic Math
  • No Programming experience.

 

Building your own Neural Network from Scratch with PythonBuilding your own Neural Network from Scratch with Python

Description

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!!!

 

Building your own Neural Network from Scratch with PythonBuilding your own Neural Network from Scratch with Python

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.

 

Course content

  • Introduction
    • Welcome
    • General Introduction
    • Why Build a Neural Network from Scratch?
    • About this Course
  • Essential Python Programming
    • Python Installation
    • Variables and Basic Operators
    • Conditional Statements
    • Loops
    • Methods
    • Objects and Classes
    • Operator Overloading
    • Method Types
    • Inheritance
    • Encapsulation
    • Polymorphism
    • Decorators
    • Generators
    • Numpy Package
    • Introduction to Matplotlib
  • Introduction to Machine Learning
    • Task – Machine Learning Development Life Cycle
    • Data – Machine Learning Development Life Cycle
    • Model – Machine Learning Development Life Cycle
    • Error Sanctioning – Machine Learning Development Life Cycle
    • Linear Regression
    • Logistic Regression
    • Linear Regression Practice
    • Logistic Regression Practice
    • Optimization
    • Performance Measurement
    • Validation and Testing
  • Softmax Regression
    • Data
    • Modeling
    • Error Sanctioning
    • Training and Optimization
    • Performance Measurement
  • Neural Networks
    • Modeling
    • Error Sanctioning
    • Training and Optimization
    • Training and Optimization Practice
    • Performance Measurement
    • Validation and Testing
    • Solving Overfitting and Underfitting
    • Shuffling
    • Ensembling
    • Weight Initialization
    • Data Imbalance
    • Learning rate decay
    • Normalization
    • Hyperparameter tuning
    • In Class Exercise

 


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