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[FTUForum.com] [UDEMY] Deep Learning Plunge into Deep Learning [FTU]

[FTUForum.com] [UDEMY] Deep Learning Plunge into Deep Learning [FTU]

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Description
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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.




File list
  • [FTUForum.com] [UDEMY] Deep Learning Plunge into Deep Learning [FTU]
  • 0. Websites you may like/How you can help Team-FTU.txt 237 B
  • 1. Introduction/1. Applications of Deep Learning.mp4 44.7 MB
  • 1. Introduction/2. What is Deep Learning.mp4 10.7 MB
  • 1. Introduction/3. Why Deep Learning.mp4 5.6 MB
  • 1. Introduction/4. Why now.mp4 16.5 MB
  • 2. Fundamentals/1. Hello World of Deep learning.mp4 5.7 MB
  • 2. Fundamentals/2. Dataset and Features.mp4 7.3 MB
  • 2. Fundamentals/3. Classification.mp4 10.1 MB
  • 3. Neural Networks/1. Perceptron.mp4 102.9 MB
  • 3. Neural Networks/2. Sigmoid Function.mp4 43 MB
  • 3. Neural Networks/3. Softmax Function.mp4 55.9 MB
  • 3. Neural Networks/4. One Hot Encoding.mp4 30.7 MB
  • 3. Neural Networks/5. Activation Functions.mp4 24.9 MB
  • 3. Neural Networks/6. Logic Gates and XOR Problem.mp4 14.3 MB
  • 4. Training Neural Networks/1. Cross Entropy.mp4 37 MB
  • 4. Training Neural Networks/10. Drop out.mp4 7.9 MB
  • 4. Training Neural Networks/11. Vanishing Gradient Problem.mp4 23.8 MB
  • 4. Training Neural Networks/2. Loss Optimization.mp4 17.1 MB
  • 4. Training Neural Networks/3. Gradient Descent.mp4 67.6 MB
  • 4. Training Neural Networks/4. Non Linear Models.mp4 27.7 MB
  • 4. Training Neural Networks/5. Feed Forward.mp4 26.9 MB
  • 4. Training Neural Networks/6. Backward Propagation.mp4 13.6 MB
  • 4. Training Neural Networks/7. Overfitting problem.mp4 30.2 MB
  • 4. Training Neural Networks/8. Early Stopping.mp4 26.1 MB
  • 4. Training Neural Networks/9. Regularization.mp4 21.4 MB
  • 5. Convolution Neural Networks/1. Need for feature extraction.mp4 41.5 MB
  • 5. Convolution Neural Networks/2. Preprocessing.mp4 10.2 MB
  • 5. Convolution Neural Networks/3. Convolution Operation.mp4 226.3 MB
  • 5. Convolution Neural Networks/4. Pooling Layer.mp4 29.9 MB
  • 5. Convolution Neural Networks/5. Flattening.mp4 15.1 MB
  • 6. Sequence Models/1. Recurrent Neural Networks.mp4 40.6 MB
  • 6. Sequence Models/2. LSTMs.mp4 18.6 MB
  • 6. Sequence Models/3. Architecture of LSTMs.mp4 20 MB
  • 6. Sequence Models/4. Forget Gate.mp4 16.9 MB
  • 6. Sequence Models/5. Learn Gate.mp4 9.6 MB
  • 6. Sequence Models/6. Remember Gate.mp4 2.4 MB
  • 6. Sequence Models/7. Use Gate.mp4 4.2 MB

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