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Master Deep Learning using Case Studies _ Beginner-Advance


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Master Deep Learning using Case Studies : Beginner-Advance
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 243 lectures (34h 23m) | Size: 10.6 GB

Master Deep Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights.

What you'll learn:
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

Requirements
Any Beginner Can Start this Course
2+2 knowledge is more than sufficient as we have covered almost everything from scratch.
Prior Knowledge of Machine Learning is beneficial , if not we have covered all required pre-requisites in the course itself.


Description
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

Who this course is for
This course is meant for anyone who wants to become a Data Scientist , Deep Learning Engineers

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Edited by Bad Karma
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