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Deep Learning and Neural Networks Python Keras

person icon Abhilash Nelson

4.3

Deep Learning and Neural Networks Python Keras

Build deep learning models with Python and Keras.

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Abhilash Nelson

English [CC]

category icon Development ,Data Science,Deep Learning

Lectures -88

Resources -4

Duration -11 hours

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Course Description

Deep Learning and Neural Networks Python Keras course is a basic to advanced crash course. The course teaches you deep learning neural networks and convolutional neural networks using Keras and Python. It is designed to skyrocket your current career prospects as this is the most in-demand skill today and for sure is the technology of the future. 

Course Overview

The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. There will be a day in the near future when deep learning models will outperform human intelligence.

Deep Learning and Machine Learning along with Data Science are a few of the most sought-after talents in the technology world nowadays. Its applications range from Google suggestions, translations, ads, movie recommendations, friend suggestions, sales and customer experience, and many more.

Learning these technologies involves a misconception that it needs prior knowledge of maths, statistics, complex algorithms, and formulas. The basic know-how is an added advantage but it is definitely not mandatory.

In this deep learning course, there is a perfect balance between learning the basic concepts along the implementation of the built-in Deep Learning Classes and functions from the Keras Library using the Python Programming Language.

These classes, functions, and APIs are just like the control pedals from the car engine, which we can use easily to build an efficient deep-learning model. Build deep learning models to solve a variety of problems, such as image classification, natural language processing, and speech recognition.

Goals

  • Understand the fundamentals of Neural Networks.

  • Master the basics of deep learning.

  • Understand the basics of machine learning.

  • Build and train deep learning models.

  • Use deep learning for problem-solving.

Prerequisites

  • A medium-configuration computer.

  • Willingness to learn in order to indulge in the world of deep learning.

Deep Learning and Neural Networks Python Keras

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction

3 Lectures
  • play icon Course Introduction and Table of Contents 11:29 11:29
  • play icon Deep Learning Overview - Theory Session - Part 1 05:51 05:51
  • play icon Deep Learning Overview - Theory Session - Part 2 06:30 06:30

Choosing Between ML or DL for the next AI project

1 Lectures
Tutorialspoint

Preparing Your Computer

2 Lectures
Tutorialspoint

Python Basics

4 Lectures
Tutorialspoint

Environment Setup

8 Lectures
Tutorialspoint

Explaining Multi-Layer Perceptron Concepts

1 Lectures
Tutorialspoint

Explaining Neural Networks Steps and Terminology

1 Lectures
Tutorialspoint

First Neural Network with Keras

1 Lectures
Tutorialspoint

Explaining Training and Evaluation Concepts

1 Lectures
Tutorialspoint

Pima Indian Model

8 Lectures
Tutorialspoint

Iris Flower Multi-Class

4 Lectures
Tutorialspoint

Sonar Returns Dataset

2 Lectures
Tutorialspoint

Sonar Performance Improvement

3 Lectures
Tutorialspoint

Boston Housing

2 Lectures
Tutorialspoint

Boston Performance Improvement

3 Lectures
Tutorialspoint

Pima Indian Dataset

4 Lectures
Tutorialspoint

Load and Predict

3 Lectures
Tutorialspoint

Checkpointing

4 Lectures
Tutorialspoint

Plotting Model Behavior History

2 Lectures
Tutorialspoint

Dropout Regularization

3 Lectures
Tutorialspoint

Learning Rate Schedule using Ionosphere Dataset

1 Lectures
Tutorialspoint

Time Based Learning Rate Schedule

2 Lectures
Tutorialspoint

Drop Based Learning Rate Schedule

2 Lectures
Tutorialspoint

Convolutional Neural Networks

2 Lectures
Tutorialspoint

MNIST Handwritten Digit Recognition Dataset

2 Lectures
Tutorialspoint

MNIST Multi-Layer Perceptron Model Development

2 Lectures
Tutorialspoint

Convolutional Neural Network Model using MNIST

2 Lectures
Tutorialspoint

Large CNN using MNIST

1 Lectures
Tutorialspoint

Load and Predict using the MNIST CNN Model

1 Lectures
Tutorialspoint

Augmentation

5 Lectures
Tutorialspoint

CIFAR-10 Object Recognition Dataset

1 Lectures
Tutorialspoint

Simple CNN using CIFAR-10 Dataset

3 Lectures
Tutorialspoint

Train and Save CIFAR-10 Model

1 Lectures
Tutorialspoint

Load and Predict using CIFAR-10 CNN Model

1 Lectures
Tutorialspoint

SOURCE CODE ATTACHED

1 Lectures
Tutorialspoint

Instructor Details

Abhilash Nelson

Abhilash Nelson

I am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications.

I am a Post Graduate Masters Degree holder in Computer Science and Engineering.

My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications.

I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design.

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