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From Zero to Hero- Mastering Neural Networks with Keras

  • Development
  • May 03, 2025
SynopsisFrom Zero to Hero: Mastering Neural Networks with Keras, avai...
From Zero to Hero- Mastering Neural Networks with Keras  No.1

From Zero to Hero: Mastering Neural Networks with Keras, available at Free, has an average rating of 4.4, with 5 lectures, based on 13 reviews, and has 2325 subscribers.

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You will learn about Introduced to Colab by Google How to Implement Deep Neural Network How to Implement Convolutional Neural Network How to Implement Recurrent Neural Network How to Implement Complex Neural Network which has both CNN and RNN layers This course is ideal for individuals who are Beginners course for people interested in learning the implementation of Neural Networks and doing real world projects It is particularly useful for Beginners course for people interested in learning the implementation of Neural Networks and doing real world projects.

Enroll now: From Zero to Hero: Mastering Neural Networks with Keras

Summary

Title: From Zero to Hero: Mastering Neural Networks with Keras

Price: Free

Average Rating: 4.4

Number of Lectures: 5

Number of Published Lectures: 5

Number of Curriculum Items: 5

Number of Published Curriculum Objects: 5

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

  • Introduced to Colab by Google
  • How to Implement Deep Neural Network
  • How to Implement Convolutional Neural Network
  • How to Implement Recurrent Neural Network
  • How to Implement Complex Neural Network which has both CNN and RNN layers
  • Who Should Attend

  • Beginners course for people interested in learning the implementation of Neural Networks and doing real world projects
  • Target Audiences

  • Beginners course for people interested in learning the implementation of Neural Networks and doing real world projects
  • In this comprehensive course, you will learn how to implement various types of neural networks using Keras, with step-by-step guidance and hands-on projects. You don’t need to set up anything on your system as everything will be done online. You will be provided with example code and practice problems to reinforce your understanding of the concepts.

    Throughout the course, you will work on four exciting projects that cover different neural network architectures and datasets. You will start by implementing and training a fully connected neural network for character classification using the popular MNIST dataset. You will then move on to creating and training a convolutional neural network (CNN) for the same dataset.

    Next, you will learn how to implement and train a multi-layer LSTM neural network for Human Activity Recognition using the WISDM dataset. Finally, you will explore how to build and train a multi-layer CNN-RNN neural network for the same dataset.

    For each project, you will be provided with code and Colab notebooks to experiment with, allowing you to practice and apply what you have learned in a real-world setting. This course is designed to take you from the basics to advanced models, so you can develop your skills and confidently implement complex neural networks.

    While a theoretical background in deep learning is expected, a basic understanding is sufficient to get started with this course. Join us now and learn how to build and train neural networks using Keras!

    Course Curriculum

    Chapter 1: Introduction

    Lecture 1: Introduction to Colab

    Chapter 2: Deep Neural Network

    Lecture 1: Beginners guide to making Deep Neural Network with Keras in Colab

    Chapter 3: Convolutional Neural Network

    Lecture 1: Deep Neural Network with Keras

    Chapter 4: Recurrent Neural network

    Lecture 1: RNN with Keras

    Chapter 5: Complex models Implementation

    Lecture 1: Complex models with Keras

    Instructors

  • From Zero to Hero- Mastering Neural Networks with Keras  No.2
    Engr. Hamza Ali Imran
    Design Engineer at Emumba Private Limited, Islamabad
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  • 2 stars: 1 votes
  • 3 stars: 1 votes
  • 4 stars: 3 votes
  • 5 stars: 8 votes
  • Frequently Asked Questions

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