Master Deep Learning using Case Studies - Beginner-Advance

Master Deep Learning using Case Studies : Beginner-Advance, available at $54.99, has an average rating of 4.4, with 243 lectures, based on 35 reviews, and has 400 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is meant for anyone who wants to become a Data Scientist , Deep Learning Engineers It is particularly useful for This course is meant for anyone who wants to become a Data Scientist , Deep Learning Engineers.

Enroll now: Master Deep Learning using Case Studies : Beginner-Advance

Summary

Title: Master Deep Learning using Case Studies : Beginner-Advance

Price: $54.99

Average Rating: 4.4

Number of Lectures: 243

Number of Published Lectures: 243

Number of Curriculum Items: 243

Number of Published Curriculum Objects: 243

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: History of Deep Learning

Lecture 3: Perceptron

Lecture 4: Multi level perceptron

Lecture 5: Neural network playground

Lecture 6: Representations

Lecture 7: Training Neural network part1

Lecture 8: Training Neural network part2

Lecture 9: Training Neural network part3

Lecture 10: Activation Function

Chapter 2: Artificial Neural Networks

Lecture 1: Introduction

Lecture 2: Deep Learning

Lecture 3: Understanding human brain

Lecture 4: Perceptron

Lecture 5: Perceptron for classifier

Lecture 6: Perceptron in depth

Lecture 7: Homogeneous co-ordinate

Lecture 8: Example for perceptron

Lecture 9: Multi classifier

Lecture 10: Neural network

Lecture 11: Input layer

Lecture 12: Output layer

Lecture 13: sigmoid function

Lecture 14: Understanding MNIST

Lecture 15: Assumptions in Neural Network

Lecture 16: Training in neural network

Lecture 17: Understanding notations

Lecture 18: Activation functions

Chapter 3: Feed forward network

Lecture 1: Introduction

Lecture 2: Online offline mode

Lecture 3: bidirectional RNN

Lecture 4: Understanding dimensions

Lecture 5: Pseudocode

Lecture 6: Pseudocode for batch

Lecture 7: Vectorised methods

Chapter 4: Backpropogation

Lecture 1: Introduction

Lecture 2: Introducing loss function

Lecture 3: back propogation training part1

Lecture 4: back propogation training part2

Lecture 5: back propogation training part3

Lecture 6: back propogation training part4

Lecture 7: back propogation training part5

Lecture 8: Sigmoid function

Lecture 9: back propogation training part6

Lecture 10: back propogation training part7

Lecture 11: back propogation training part8

Lecture 12: back propogation training part9

Lecture 13: back propogation training part10

Lecture 14: Pseudocode

Lecture 15: SGD

Lecture 16: Finding global minima

Lecture 17: Training for batches

Chapter 5: Regularisation

Lecture 1: Introduction to regularisation

Lecture 2: Dropouts part1

Lecture 3: Dropouts part2

Lecture 4: Batch normalisation part1

Lecture 5: Batch normalisation part2

Lecture 6: Batch normalisation part3

Lecture 7: Introducing Tensorflow

Lecture 8: Introducing keras

Chapter 6: Convolution Neural Network

Lecture 1: Introduction

Lecture 2: Applications for CNN

Lecture 3: Idea behind CNN part1

Lecture 4: Idea behind CNN part2

Lecture 5: Images

Lecture 6: Video

Lecture 7: Convolution part1

Lecture 8: Convolution part2

Lecture 9: stride and padding

Lecture 10: padding

Lecture 11: formulas

Lecture 12: weight and bias

Lecture 13: feature map

Lecture 14: pooling

Lecture 15: combining network

Chapter 7: Practical on CNN

Lecture 1: Introduction

Lecture 2: Introducing VGG16

Lecture 3: Case Study Part1

Lecture 4: Case Study Part2

Lecture 5: Case Study Part3

Lecture 6: Case Study Part4

Lecture 7: Case Study Part5

Chapter 8: Real World Project (Project1: Playing With Real World Nat)

Lecture 1: Introduction

Lecture 2: Case Study Part1

Lecture 3: Case Study Part2

Lecture 4: Case Study Part3

Lecture 5: Case Study Part4

Lecture 6: Case Study Part5

Lecture 7: Case Study Part6

Lecture 8: Case Study Part7

Lecture 9: Case Study Part8

Lecture 10: Case Study Part9

Instructors

Geekshub Pvt Ltd
BigData and Analytics

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 5 votes

4 stars: 10 votes

5 stars: 20 votes

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