TGx:Udemy | Deep Learning : Plunge into Deep Learning [FTU] TGx Exclusive

Hot Picks

First Cam

New Movies

1080p x265

New TV

Misc

Torrent details for "Udemy | Deep Learning : Plunge into Deep Learning [FTU] TGx Exclusive" Log in to bookmark

Quicknav > Description Read comments 0 Post guest comment

Torrent details

Name:

Udemy | Deep Learning : Plunge into Deep Learning [FTU] TGx Exclusive

Controls:

Report Torrent

Torrent verified by Prom3th3uS

Category:

Other > Training

Language:

English

Total Size:

1.08 GB

Info Hash:

293a7f57b5be044e51940205653c3449e33670e5

Added By:

Prom3th3uS :_verified_uploader:

Added:

17-07-2019 17:53

Views:

595

Update stats Post guest comment 0

Health:

Seeds:

Leechers:

Completed:

100

File	Size
0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url	328.00 B
0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	286.00 B
0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url	163.00 B
0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url	239.00 B
0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url	294.00 B
0. Websites you may like/How you can help Team-FTU.txt	237.00 B
1. Introduction/1. Applications of Deep Learning.mp4	44.71 MB
1. Introduction/2. What is Deep Learning.mp4	10.69 MB
1. Introduction/3. Why Deep Learning.mp4	5.63 MB
1. Introduction/4. Why now.mp4	16.47 MB
2. Fundamentals/1. Hello World of Deep learning.mp4	5.72 MB
2. Fundamentals/2. Dataset and Features.mp4	7.34 MB
2. Fundamentals/3. Classification.mp4	10.13 MB
3. Neural Networks/1. Perceptron.mp4	102.93 MB
3. Neural Networks/2. Sigmoid Function.mp4	43.02 MB
3. Neural Networks/3. Softmax Function.mp4	55.92 MB
3. Neural Networks/4. One Hot Encoding.mp4	30.71 MB
3. Neural Networks/5. Activation Functions.mp4	24.89 MB
3. Neural Networks/6. Logic Gates and XOR Problem.mp4	14.30 MB
4. Training Neural Networks/1. Cross Entropy.mp4	36.99 MB
4. Training Neural Networks/10. Drop out.mp4	7.92 MB
4. Training Neural Networks/11. Vanishing Gradient Problem.mp4	23.84 MB
4. Training Neural Networks/2. Loss Optimization.mp4	17.11 MB
4. Training Neural Networks/3. Gradient Descent.mp4	67.55 MB
4. Training Neural Networks/4. Non Linear Models.mp4	27.69 MB
4. Training Neural Networks/5. Feed Forward.mp4	26.94 MB
4. Training Neural Networks/6. Backward Propagation.mp4	13.60 MB
4. Training Neural Networks/7. Overfitting problem.mp4	30.17 MB
4. Training Neural Networks/8. Early Stopping.mp4	26.06 MB
4. Training Neural Networks/9. Regularization.mp4	21.39 MB
5. Convolution Neural Networks/1. Need for feature extraction.mp4	41.50 MB
5. Convolution Neural Networks/2. Preprocessing.mp4	10.18 MB
5. Convolution Neural Networks/3. Convolution Operation.mp4	226.33 MB
5. Convolution Neural Networks/4. Pooling Layer.mp4	29.92 MB
5. Convolution Neural Networks/5. Flattening.mp4	15.11 MB
6. Sequence Models/1. Recurrent Neural Networks.mp4	40.64 MB
6. Sequence Models/2. LSTMs.mp4	18.65 MB
6. Sequence Models/3. Architecture of LSTMs.mp4	20.03 MB
6. Sequence Models/4. Forget Gate.mp4	16.91 MB
6. Sequence Models/5. Learn Gate.mp4	9.62 MB
6. Sequence Models/6. Remember Gate.mp4	2.41 MB
6. Sequence Models/7. Use Gate.mp4	4.17 MB

Description

For More Udemy Free Courses >>> https://ftuforum.com/
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.ftuforum.com/

Learn to create Deep Learning models starting from basics

Created by: Muni Kumar Gopu V R
Last updated: 1/2019
Language: English
Torrent Contains: 42 Files, 7 Folders
Course Source: https://www.udemy.com/plunge-into-deep-learning/

What you'll learn

• Understand the intuition behind Artificial Neural Networks
• Build Deep Learning Models
• Convolution Neural Networks
• Sequence Models

Course content
all 36 lectures 02:29:53

Requirements

• Just some high school mathematics
• Basic linear algebra and calculus

Description

Interested in the field of Machine Learning and Deep Learning? Then this course is for you!

This course is designed in a very simple and easily understandable content.

You might have seen lots of buzz on deep learning and you want to figure out where to start and explore.

This course is designed exactly for people like you!

If basics are strong, we can do bigger things with ease.

My focus in this course is to build complicated things starting from very basics

In this course, I will cover the following things

• Session 1 – Introductory material on Deep learning, its applications and significance.

• Session 2 - Introduces the fundamental building block of deep learning

• Session 3 – Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models

• Session 4 – Training of Neural Networks: Cross Entropy, Loss Function, Gradient descent Algorithm, Non-Linear Models, Feed Forward, Backward propagation, Overfitting problem, Early stopping, Regularization, drop out and Vanishing Gradient problem.

• Session 5 – Convolution Neural Networks: Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks.

• Session 6 – Sequence Models: Recurrent Neural Networks, LSTMs

Are there any course requirements or prerequisites?

• Just some high school mathematics level.

Who this course is for:

• Anyone interested in Machine Learning and Deep Learning
• Students who have high school knowledge in mathematics and who want to start learning Deep Learning
• Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning
• Any students in college who want to start a career in Data Science
• Any data analysts who want to level up in Machine Learning and Deep Learning
• Any people who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Learning tools.
• Build a foundation on the principles of Deep Learning to understand the latest trends.