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Master Deep Learning using Case Studies : Beginner-Advance

Master Deep Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights.
Instructor:
Geekshub Pvt Ltd
82 students enrolled
English [Auto]
Master Deep Learning on Python
Master Machine Learning on Python
Learn to use MatplotLib for Python Plotting
Learn to use Numpy and Pandas for Data Analysis
Learn to use Seaborn for Statistical Plots
Learn All the Mathmatics Required to understand Deep Learning Algorithms
Implement Deep Learning Algorithms along with Mathematic intutions
Real world projects of Deep Learning
Learning End to End Data Science Solutions
All Advanced Level Deep Learning Algorithms and Techniques like Regularisations , Dropout and many more included
Learn All Statistical concepts To Make You Ninza in Deep Learning
Real World Case Studies
Keras
Transfer Learning
Artifical Neural Network
Convolution Neural Network
Recurrent Neural Network
Feed Forward Network
Backpropogation

Wants to become a good Data Scientist?  Then this is a right course for you.

This course has been designed by IIT professionals who have mastered in Mathematics and Data Science.  We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.

We will walk you step-by-step into the World of Deep Learning. With every tutorial you will develop new skills and improve your understanding towards the challenging yet lucrative sub-field of Data Science from beginner to advance level.

We have solved few real world projects as well during this course and have provided complete solutions so that students can easily implement what have been taught.

We have covered following topics in detail in this course:

1. Introduction

2. Artificial Neural Network

3. Feed forward Network

4. Backpropogation

5. Regularisation

6. Convolution Neural Network

7. Practical on CNN

8. Real world project1

9. Real world project2

10 Transfer Learning

11. Recurrent Neural Networks

12. Advanced RNN

13. Project(Help NLP)

14. Generate Automatic Programming code

15. Pre- req : Python, Machine Learning

Introduction

1
Introduction
2
History of Deep Learning
3
Perceptron
4
Multi level perceptron
5
Neural network playground
6
Representations
7
Training Neural network part1
8
Training Neural network part2
9
Training Neural network part3
10
Activation Function

Artificial Neural Networks

1
Introduction
2
Deep Learning
3
Understanding human brain
4
Perceptron
5
Perceptron for classifier
6
Perceptron in depth
7
Homogeneous co-ordinate
8
Example for perceptron
9
Multi classifier
10
Neural network
11
Input layer
12
Output layer
13
sigmoid function
14
Understanding MNIST
15
Assumptions in Neural Network
16
Training in neural network
17
Understanding notations
18
Activation functions

Feed forward network

1
Introduction
2
Online offline mode
3
bidirectional RNN
4
Understanding dimensions
5
Pseudocode
6
Pseudocode for batch
7
Vectorised methods

Backpropogation

1
Introduction
2
Introducing loss function
3
back propogation training part1
4
back propogation training part2
5
back propogation training part3
6
back propogation training part4
7
back propogation training part5
8
Sigmoid function
9
back propogation training part6
10
back propogation training part7
11
back propogation training part8
12
back propogation training part9
13
back propogation training part10
14
Pseudocode
15
SGD
16
Finding global minima
17
Training for batches

Regularisation

1
Introduction to regularisation
2
Dropouts part1
3
Dropouts part2
4
Batch normalisation part1
5
Batch normalisation part2
6
Batch normalisation part3
7
Introducing Tensorflow
8
Introducing keras

Convolution Neural Network

1
Introduction
2
Applications for CNN
3
Idea behind CNN part1
4
Idea behind CNN part2
5
Images
6
Video
7
Convolution part1
8
Convolution part2
9
stride and padding
10
padding
11
formulas
12
weight and bias
13
feature map
14
pooling
15
combining network

Practical on CNN

1
Introduction
2
Introducing VGG16
3
Case Study Part1
4
Case Study Part2
5
Case Study Part3
6
Case Study Part4
7
Case Study Part5

Real World Project (Project1: Playing With Real World Nat)

1
Introduction
2
Case Study Part1
3
Case Study Part2
4
Case Study Part3
5
Case Study Part4
6
Case Study Part5
7
Case Study Part6
8
Case Study Part7
9
Case Study Part8
10
Case Study Part9
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Certificate of Completion
Master Deep Learning using Case Studies : Beginner-Advance
Category:
Data Science
Teacher
Daniel W
Price:
$218.98 $159
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