Deep Learning: Getting Started

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Kumaran Ponnambalam

1:08:56

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1 Introduction

01 Getting started with deep learning.mp4

00:54

02 Prerequisites for the course.mp4

02:18

03 Setting up the environment.mp4

02:31

2 Introduction to Deep Learning

04 What is deep learning.mp4

01:33

05 Linear regression.mp4

02:40

06 An analogy for deep learning.mp4

02:45

07 The perceptron.mp4

01:35

08 Artificial neural networks.mp4

02:34

09 Training an ANN.mp4

02:24

3 Neural Network Architecture

10 The input layer.mp4

02:53

11 Hidden layers.mp4

01:47

12 Weights and biases.mp4

02:24

13 Activation functions.mp4

01:56

14 The output layer.mp4

01:26

4 Training a Neural Network

15 Setup and initialization.mp4

02:43

16 Forward propagation.mp4

01:14

17 Measuring accuracy and error.mp4

02:12

18 Back propagation.mp4

02:08

19 Gradient descent.mp4

01:21

20 Batches and epochs.mp4

02:22

21 Validation and testing.mp4

01:28

22 An ANN model.mp4

01:39

5 Deep Learning Example 1

23 The Iris classification problem.mp4

01:25

24 Input preprocessing.mp4

02:37

25 Creating a deep learning model.mp4

02:30

26 Training and evaluation.mp4

02:45

27 Saving and loading models.mp4

01:07

28 Predictions with deep learning models.mp4

01:30

6 Deep Learning Example 2

29 Spam classification problem.mp4

01:40

30 Creating text representations.mp4

01:58

31 Building a spam model.mp4

01:27

32 Predictions for text.mp4

01:22

7 Deep Learning Exercise

33 Exercise problem statement.mp4

02:21

34 Preprocessing RCA data.mp4

01:06

35 Building the RCA model.mp4

00:50

36 Predicting root causes with deep learning.mp4

00:54

8 Conclusion

37 Extending your deep learning education.mp4

00:37

Description

Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts and build models quickly. This course aims to provide a simplified path to studying the basics of deep learning and becoming productive quickly. Instructor Kumaran Ponnambalam starts off with an intro to deep learning, including artificial neural networks and architectures. He navigates through various building blocks of neural networks with simple and easy to understand explanations. Kumaran also builds code in Keras to implement these building blocks. He then pulls it all together with an end-to-end exercise. Finally, test what you learned with a deep learning problem and compare your solution with Kumaran’s.

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Deep Learning

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.