English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 08m | 607 MB

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.

## Table of Contents

**Introduction**

1 Getting started with deep learning

2 Prerequisites for the course

3 Setting up the environment

**Introduction to Deep Learning**

4 What is deep learning

5 Linear regression

6 An analogy for deep learning

7 The perceptron

8 Artificial neural networks

9 Training an ANN

**Neural Network Architecture**

10 The input layer

11 Hidden layers

12 Weights and biases

13 Activation functions

14 The output layer

**Training a Neural Network**

15 Setup and initialization

16 Forward propagation

17 Measuring accuracy and error

18 Back propagation

19 Gradient descent

20 Batches and epochs

21 Validation and testing

22 An ANN model

**Deep Learning Example 1**

23 The Iris classification problem

24 Input preprocessing

25 Creating a deep learning model

26 Training and evaluation

27 Saving and loading models

28 Predictions with deep learning models

**Deep Learning Example 2**

29 Spam classification problem

30 Creating text representations

31 Building a spam model

32 Predictions for text

**Deep Learning Exercise**

33 Exercise problem statement

34 Preprocessing RCA data

35 Building the RCA model

36 Predicting root causes with deep learning

**Conclusion**

37 Extending your deep learning education

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