3 real world deep learning projects

3 real world deep learning projects, available at $22.99, has an average rating of 3.8, with 9 lectures, based on 27 reviews, and has 8321 subscribers.

You will learn about Apply deep learning in real use case projects Learn about the different Layers present in a Neural Networks. Learn about different Activation Functions used in a Neural Network. Learn about the Recurrent Neural Networks. Implement the LSTMs to solve Sequential Problems. Understand Neural and Convolutional Neural Networks Practice questions after doing each projects This course is ideal for individuals who are People who are interested in the field of deep learning It is particularly useful for People who are interested in the field of deep learning.

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Summary

Title: 3 real world deep learning projects

Price: $22.99

Average Rating: 3.8

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

Apply deep learning in real use case projects

Learn about the different Layers present in a Neural Networks.

Learn about different Activation Functions used in a Neural Network.

Learn about the Recurrent Neural Networks.

Implement the LSTMs to solve Sequential Problems.

Understand Neural and Convolutional Neural Networks

Practice questions after doing each projects

Who Should Attend

People who are interested in the field of deep learning

Target Audiences

People who are interested in the field of deep learning

What is this course all about?

Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will learn about different concepts through doing real life projects.These projects will help you to get an edge in college projects and interviews.

We will also add practice questions after each lecture so that you can have good understanding about the concepts.

What is deep learning?

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain鈥攁lbeit far from matching its ability鈥攁llowing it to 鈥渓earn鈥?from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

Deep learning is increasingly dominating technology and has major implications for society.

From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.

But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Deep learning is now used in most areas of technology, business, and entertainment. And it’s becoming more important every year.

How does deep learning work?

Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.

Is it really that simple? mmm OK, it’s actually a tiny bit more complicated than that 馃槈 but that’s the core idea, and everything else literally everything else in deep learning is just clever ways of putting together these fundamental building blocks. That doesn’t mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.

Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning I mean, really learn deep learning, not just have superficial knowledge from a youtube video by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Bitcoin price prediction using deep learning

Lecture 1: Video 1

Lecture 2: Video 2

Lecture 3: Video 3

Lecture 4: Video 4

Chapter 3: Chess pieces classification using deep learning

Lecture 1: Video 1

Lecture 2: Video 2

Chapter 4: Predicting employee retention using deep learning

Lecture 1: Video 1

Lecture 2: Video 2

Instructors

Neuralclass Academy
Learn from the experts in the field!

Rating Distribution

1 stars: 1 votes

2 stars: 4 votes

3 stars: 1 votes

4 stars: 8 votes

5 stars: 13 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!