Deep Learning- Convolutional Neural Networks for developers

Deep Learning: Convolutional Neural Networks for developers, available at $44.99, has an average rating of 3.95, with 12 lectures, based on 42 reviews, and has 4516 subscribers.

You will learn about Convolutional neural network architectures Computer vision algorithims How to implement a Deep Neural Network from scratch How back-propagation algorithm works How to search similar images How to build multi task models This course is ideal for individuals who are Developers curious about deep learning and AI It is particularly useful for Developers curious about deep learning and AI.

Enroll now: Deep Learning: Convolutional Neural Networks for developers

Summary

Title: Deep Learning: Convolutional Neural Networks for developers

Price: $44.99

Average Rating: 3.95

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Convolutional neural network architectures

Computer vision algorithims

How to implement a Deep Neural Network from scratch

How back-propagation algorithm works

How to search similar images

How to build multi task models

Who Should Attend

Developers curious about deep learning and AI

Target Audiences

Developers curious about deep learning and AI

This course will teach you Deep learning focusing on Convolution Neural Net architectures. It is structured to help you genuinely learn Deep Learning by starting from the basics until advanced concepts. We will begin learning what it is under the hood of Deep learning frameworks like Tensorflow and Pytorch, then move to advanced Deep learning Architecture with Pytorch.

During our journey, we will also have projects exploring some critical concepts of Deep learning and computer vision, such as: what is an image; what are convolutions; how to implement a vanilla neural network; how back-propagation works; how to use transfer learning and more.

All examples are written in Python and Jupyter notebooks with tons of comments to help you to follow the implementation. Even if you don’t know Python well, you will be able to follow the code and learn from the examples.

The advanced part of this project will require GPU but don’t worry because those examples are ready to run on Google Colab with just one click, no setup required, and it is free! You will only need to have a Google account.

By following this course until the end, you will get insights, and you will feel empowered to leverage all recent innovations in the Deep Learning field to improve the experience of your projects.

Course Curriculum

Chapter 1: CNN for devs

Lecture 1: Introduction

Lecture 2: Source code

Lecture 3: Working with images and Numpy

Lecture 4: Image convolution with Numpy

Lecture 5: Building a neural network from scratch – part 1

Lecture 6: Building a neural network from scratch – part 2

Lecture 7: Building a neural network with PyTorch

Lecture 8: Convolution Neural Network with PyTorch

Lecture 9: CNN and Autoencoder – image interpolation

Lecture 10: Image similarity – Image reverse search engine

Lecture 11: Developing a multitasking model

Lecture 12: Playing with Generative Adversarial Networks (GAN)

Instructors

Alexsandro Souza
Tech Lead

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 5 votes

4 stars: 12 votes

5 stars: 21 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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