{ C Language } Deep Learning From Ground Up™

{ C Language } Deep Learning From Ground Up™, available at $64.99, has an average rating of 4.3, with 52 lectures, based on 145 reviews, and has 1999 subscribers.

You will learn about Build Neural Network for Handwriting Recognition Build Neural Networks from scratch without libraries in C Understand the fundamentals of deep neural networks from a C perspective Build a C Deep Learning library This course is ideal for individuals who are 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 we move on to see how to use readily available libraries. or If you already have some experience with deep learning and want to see how to develop models in c you can also join this course. The course gives an in-depth training on how to develop deep learning models using the c language. It is particularly useful for 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 we move on to see how to use readily available libraries. or If you already have some experience with deep learning and want to see how to develop models in c you can also join this course. The course gives an in-depth training on how to develop deep learning models using the c language.

Enroll now: { C Language } Deep Learning From Ground Up™

Summary

Title: { C Language } Deep Learning From Ground Up™

Price: $64.99

Average Rating: 4.3

Number of Lectures: 52

Number of Published Lectures: 51

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 51

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Build Neural Network for Handwriting Recognition
Build Neural Networks from scratch without libraries in C
Understand the fundamentals of deep neural networks from a C perspective
Build a C Deep Learning library

Who Should Attend

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 we move on to see how to use readily available libraries.
If you already have some experience with deep learning and want to see how to develop models in c you can also join this course. The course gives an in-depth training on how to develop deep learning models using the c language.

Target Audiences

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 we move on to see how to use readily available libraries.
If you already have some experience with deep learning and want to see how to develop models in c you can also join this course. The course gives an in-depth training on how to develop deep learning models using the c language.

Welcome to the { C Language } Deep Learning From Ground Up™course.

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 codeshowing 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 we move on to see how to use readily available libraries.

If you already have some experience with deep learning and want to see how to develop models in c you can also join this course.The course gives an in-depth training on how to develop deep learning models using the c language.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Deep Learning

Chapter 2: Set Up

Lecture 1: Setting up an Integrated Development Environment (IDE)

Chapter 3: Introduction to Neural Networks

Lecture 1: The Single Input Single Output Neural Network

Lecture 2: Coding : Single Input Single Output Neural Network

Lecture 3: The Single Input Multiple Output Neural Network

Lecture 4: Coding : Single Input Multiple Output Neural Network

Lecture 5: The Multiple Input Single Output Neural Network

Lecture 6: Coding : Multiple Input Single Output Neural Network

Lecture 7: The Multiple Input Multiple Output Neural Network

Lecture 8: Coding : Multiple Input Multiple Output Neural Network

Lecture 9: The Hidden Layer Neural Network

Lecture 10: Coding : The Hidden Layer Neural Network

Lecture 11: Comparing and Finding Error

Lecture 12: Coding : Finding Error

Lecture 13: Understanding data representation in Machine Learning

Lecture 14: Understanding the "Learning" in Machine Learning

Lecture 15: Coding : Brute-force Learning

Lecture 16: Introduction to Gradient Descent

Lecture 17: Functional Description of a Biological Neuron

Chapter 4: Introduction to Neural Network (Part 2)

Lecture 1: Case Study : Building a Neural Network to Predict Muscle Gain

Lecture 2: Coding : Normalizing Datasets

Lecture 3: Coding : Random Initialization of Weights

Lecture 4: Understanding Activation Functions

Lecture 5: Coding : Forward Propagation

Lecture 6: Basics of Calculus

Chapter 5: Logistic Regression

Lecture 1: Case Study : Building a Neural Network to Detect Cats

Chapter 6: Deep Neural Networks

Lecture 1: Internals of a 2 layer Neural Network

Lecture 2: Understanding Computational Graphs

Lecture 3: Updating Parameters Effectively

Lecture 4: Understanding the Importance of Vectorization

Lecture 5: Summary of Back-propagation and Forward-propagation

Lecture 6: Initializing Parameters Effectively

Lecture 7: Understanding Layers and Units

Lecture 8: Understanding the Shapes

Lecture 9: Understanding Broadcasting in Programming

Chapter 7: Improving Neural Networks with Regularization Techniques

Lecture 1: Overfitting and Underfitting

Chapter 8: Building a Complete Neural Network Library for Predicting Handwritten Numbers

Lecture 1: Coding : Defining our Neural Network Structure

Chapter 9: Building Our Neural Network Library Utility Functions

Lecture 1: Coding : Defining our Data Object Structure

Lecture 2: Coding : Implementing a Function to Read Data From a File

Lecture 3: Coding : Implementing a Function to Parse our Data

Lecture 4: Coding : Implementing more Utility Functions

Chapter 10: Building Our Neural Network Library Engine

Lecture 1: Coding : Implementing the Forward Propagation Function

Lecture 2: Coding : Implementing the Back Propagation Function

Lecture 3: Coding : Implementing the NNPredict Function

Lecture 4: Coding : Implementing the NNBuild and NNTrain Functions

Lecture 5: Coding : Implementing the NNSaveModel and NNLoadModel Functions

Lecture 6: Coding : Implementing the NNPrint Function

Chapter 11: Testing our Neural Network Library

Lecture 1: Coding : Training a Model to Predict Handwritten Digits

Lecture 2: Coding : Testing our Model

Lecture 3: Coding : Running Inference with our Model

Chapter 12: Closing

Lecture 1: Closing Remarks

Instructors

Israel Gbati
Embedded Firmware Engineer
BHM Engineering Academy
21st Century Engineering Academy

Rating Distribution

1 stars: 7 votes
2 stars: 1 votes
3 stars: 18 votes
4 stars: 39 votes
5 stars: 80 votes

Frequently Asked Questions

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