Neural Networks Made Easy

Neural Networks Made Easy, available at Free, has an average rating of 4.65, with 15 lectures, based on 51 reviews, and has 7643 subscribers.

You will learn about Neural Network Fundamentals This course is ideal for individuals who are People interested in learning how neural networks work It is particularly useful for People interested in learning how neural networks work.

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Summary

Title: Neural Networks Made Easy

Price: Free

Average Rating: 4.65

Number of Lectures: 15

Number of Published Lectures: 13

Number of Curriculum Items: 15

Number of Published Curriculum Objects: 13

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Neural Network Fundamentals

Who Should Attend

People interested in learning how neural networks work

Target Audiences

People interested in learning how neural networks work

Wanna understand deep learning and neural networks so well, you could code them from scratch? In this course, we’ll do exactly that.

The course starts by motivating and explaining perceptrons, and then gradually works its way toward deriving and coding a multiclass neural network with stochastic gradient descent that can recognize hand-written digits from the famous MNIST dataset.

Course Goals

This course is all about understanding the fundamentals of neural networks. So, it does not discuss TensorFlow, PyTorch, or any other neural network libraries. However, by the end of this course, you should understand neural networks so well that learning TensorFlow and PyTorch should be a breeze!

Challenges

In this course, I present a number of coding challenges inside the video lectures. The general approach is, we’ll discuss an idea and the theory behind it, and then you’re challenged to implement the idea / algorithm in Python. I’ll discuss my solution to every challenge, and my code is readily available on github.

Prerequisites

In this course, we’ll be using Python, NumPy, Pandas, and good bit of calculus. ..but don’t let the math scare you. I explain everything in great detail with examples and visuals.

If you’re rusty on your NumPy or Pandas, check out my free courses Python NumPy For Your Grandma and Python Pandas For Your Grandpa.

Course Curriculum

Chapter 1: 1. Introduction

Lecture 1: Introduction

Lecture 2: Prereqs

Chapter 2: 2. Perceptron

Lecture 1: 2.1 MNIST Dataset

Lecture 2: 2.2 Perceptron Model

Lecture 3: 2.3 Perceptron Learning Algorithm

Lecture 4: 2.4 Pocket Algorithm

Lecture 5: 2.5 Multiclass Support

Lecture 6: 2.6 Perceptron To Neural Network

Chapter 3: 3. Neural Network

Lecture 1: 3.1 Simple Images

Lecture 2: 3.2 Random Weights

Lecture 3: 3.3 Gradient Descent

Lecture 4: 3.4 Multiclass Support

Lecture 5: 3.5 Deep Learning

Instructors

Ben Gorman
Data Scientist

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 6 votes
4 stars: 13 votes
5 stars: 30 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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