{ C Language } Deep Learning From Ground Up™

Build Artificial Intelligence Applications in C

7+ hours | Complete Source Code Included

Are you tired of hearing about deep learning and not knowing how to properly get started?

Do you already know how to write basic c code?

Here’s an overview of what you’re getting in this ground up course...

Introduction to Neural Networks

This course completely demystifies Neural Networks. The course starts from the complete basics as if you were a 5 year old. You will master and develop your own building blocks for deep neural networks. Building blocks such as :

The Single-Input-Single-Output Neural Network
The Single-Input-Multiple-Output Neural Network
The Multiple-Input-Single-Output Neural Network
The Multiple-Input-Multiple-Output Neural Network
The Hidden Layer Neural Network

The Internals of a Deep Learning Engine

After mastering the fundamental building blocks of neural networks, the course then goes on to treat the internals of a deep learning engine from a first principles approach. We shall look at topics such as:

Gradient Descent
The Loss Function
The Activation Function
Forward Propagation
Back Propagation
Training
Computational Graphs
Weights Normalization and Randomization

… and much more.

Building your own a Complete Neural Network Library for Predicting Handwritten Numbers.

The latter part of the course puts everything together to develop a complete neural network library for predicting handwritten numbers. You shall be guided step-by-step throughout this process. We shall start by implementing the utilities functions and data structures. Specifically, we shall:

Define our neural network’s data structure
Define our data object structure
Implement a function for reading data from a file
Implement a function for parsing the data

Then we shall go on to implement the various modules of the neural network engine. We shall implement:

The Forward Propagation function
The Back Propagation function
The Prediction function
The Training function

Once all of this is thoroughly implemented we then go on to….

Train our model
Test our model by running some predictions

Taken by 1200+ Students with 70+ Reviews

Here is what one student had to say about the course :

“This is an amazing course giving you the oversight of AI background. It is very obvious that the instructor is highly qualified which you feel throughout the course. So, long story in short : [Highly Recommended]”

We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch in c language.

We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network all the way to building fully functions deep learning models using c language only

By the end of this course you will be able to build neural networks from scratch without libraries, you will be able to understand the fundamentals of deep learning from a c language perspective and you will also be able to build your own deep learning library in c.

If you are new to machine learning and deep learning, this course is for you. The course starts from the very basic building block of neural network and teaches you how to build your own neural network using c language before you move on to use readily available libraries.

Preview

Lesson : Coding : Brute-force Learning

Introduction
Introduction to Deep Learning
Set Up
Setting up an Integrated Development Environment (IDE)s
Introduction to Neural Networks
The Single Input Single Output Neural Network
Coding : Single Input Single Output Neural Network
The Single Input Multiple Output Neural Network
Coding : Single Input Multiple Output Neural Network
The Multiple Input Single Output Neural Network
Coding : Multiple Input Single Output Neural Network
The Multiple Input Multiple Output Neural Network
Coding : Multiple Input Multiple Output Neural Network
The Hidden Layer Neural Network
Coding : The Hidden Layer Neural Network
Comparing and Finding Error
Coding : Finding Error
Understanding data representation in Machine Learning
Understanding the "Learning" in Machine Learning
Coding : Brute-force Learning
Introduction to Gradient Descent
Functional Description of a Biological Neuron
Introduction to Neural Network (Part 2)
Case Study : Building a Neural Network to Predict Muscle Gain
Coding : Normalizing Datasets
Coding : Random Initialization of Weights
Understanding Activation Functions
Coding : Forward Propagation
Basics of Calculus

Logistic Regression
Case Study : Building a Neural Network to Detect Cats

Deep Neural Networks
Internals of a 2 layer Neural Network
Understanding Computational Graphs
Updating Parameters Effectively
Understanding the Importance of Vectorization
Summary of Back-propagation and Forward-propagation
Initializing Parameters Effectively
Understanding Layers and Units
Understanding the Shapes
Understanding Broadcasting in Programming
Improving Neural Networks with Regularization Techniques
Overfitting and Underfitting

Building a Complete Neural Network Library for Predicting Handwritten Numbers
Coding : Defining our Neural Network Structure

Building Our Neural Network Library Utility Functions
Coding : Defining our Data Object Structure
Coding : Implementing a Function to Read Data From a File
Coding : Implementing a Function to Parse our Data
Coding : Implementing more Utility Functions

Building Our Neural Network Library Engine
Coding : Implementing the Forward Propagation Function
Coding : Implementing the Back Propagation Function
Coding : Implementing the NNPredict Function
Coding : Implementing the NNBuild and NNTrain Functions
Coding : Implementing the NNSaveModel and NNLoadModel Functions
Coding : Implementing the NNPrint Function

Testing our Neural Network Library
Coding : Training a Model to Predict Handwritten Digits
Coding : Testing our Model
Coding : Running Inference with our Model

A little about me : Israel Ninsaw Gbati

Some of you may have taken some of my embedded systems courses from other online platforms.

This is my private channel.

I have been writing embedded firmware for years, I have built embedded devices like consumer products and robotic arms.

Till date I have

trained over 75,000 students in embedded

firmware development online till date

...including third year undergraduate university students in-person.

If you have taken any of my courses before you will know I start from the absolute basics, I do not assume that the student has any prior knowledge of the topic under discussion. You will also know that by the end of the course you understand the functions of every register used in developing the particular firmware or driver.

This method is the same for all of my published embedded systems courses.

Our courses have been reviewed by 1000+ students

Here are some of the reviews