Deep Learning Bootcamp with 4 Capstone Projects
- Description
- Curriculum
- FAQ
- Reviews
Are you ready to master Deep Learning skills?
Deep Learning is a technology using which we can solve highly computational problems such as Image Processing, Image Classification, Image Segmentation, Image tagging, sound classification, video analysis etc.
Deep Learning is becoming a buzzword these days, and If you want to learn Deep Learning then It is very Important for you that you should have a proper plan regarding that.
Before Learning Deep Learning you must have learned Machine Learning and must possess good knowledge of Python programming language.
If you want to build super-powerful applications in Deep Learning.
Then, you are at the right place.
This course will provide you with in-depth knowledge on a very hot topic i.e., Deep Learning.
The purpose of this course is to provide you with knowledge of key aspects of Deep Learning without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.
This course will cover the following topics:-
1. Deep Learning (DL).
2. Artificial Neural Network (ANN).
3. Convolutional Neural Network (CNN).
4. Recurrent Neural Network. (RCN)
This course will take you through the basics to an advanced level in all the mentioned four topics.
After taking this course, you will be confident enough to work independently on any projects on these topics.
There are lots and lots of exercises for you to practice In this Deep Learning Course and also a 5 Bonus Deep Learning Project “Stock Market Prediction“, “Fruits Identification System“, “Face Expression Recognizer“, “Detecting Pneumonia from Chest X-rays”, and “Optimizing Crop Production”.
In this Optimizing Crop Production, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend best Crops to Farmers to Increase their Productivity.
In this Detecting Pneumonia from X-rays project, you will learn how to solve Image Classification Tasks using Deep Neural Networks such as ResNet which is a High Level CNN Architectures.
In this Stock Market Prediction project, you will learn to analyze and the Stock Market Prices using Time Series Forecasting, Advanced Deep Learning Models and different Statistical features.
In this Fruits Recognition project, you will learn how to solve a complicated Image Classification Task with Multiple Classes using various Deep Learning Architectures and Compare the Result.
In this Face Expression Recognizer project, you will learn to use Computer Vision Techniques to detect Human Emotions such as Angry, Sad, Happy, Disgust, Fear etc. to build a Facial Emotion Detector.
You will have access to all the resources used in this course.
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1
Path to Deep Learning -
2
Introduction to Neural Networks
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3
Introduction to Activation functions
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4
Sigmoid and Tanh Activation Functions
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5
Relu, and Leaky Relu, Activation Functions
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6
When to use Sigmoid and Softmax
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7
Introduction to Gradient Descent
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8
Batch vs Stochastic Gradient Descent
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9
Introduction to Optimizers
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10
Dropout and why do we need it
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11
Hyper parameter Tuning in Neural Networks
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12
Introduction to Batch Normalization
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13
Introduction to Tensorflow 2.0 Part 1
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14
Introduction to Tensorflow 2.0 Part 2
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15
Implementing a basic neural network
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16
Improving a Neural network
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17
Quiz on Introduction To Neural Network
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18
Introduction to Convolution Neural Network
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19
Convolution Operation in CNN
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20
Padding and Pooling
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21
Data Augmentation
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22
Understanding CNN end to end
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23
Implementing Data Processing on Image Data
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24
Implementing CNN using Tensorflow
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25
Introduction to CNN Architectures
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26
Introduction to Transfer Learning
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27
Implementing ResNet and Inception Network
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28
Industry relevance
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29
Quiz on Convolution Neural Network
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38
Understanding the Dataset
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39
Understanding the Problem Statement
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40
Setting up environment
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41
Getting and Parsing Dataset
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42
Loading and Transforming Image Data
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43
Creating a Tensorflow Dataset Object
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44
Introduction to ResNet
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45
Building a Tensorflow Model
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46
Understanding Model Checkpoints
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47
Training the Model
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48
Interpreting the Results
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49
Saving the Trained Model
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50
Evaluating the Model on Test Data
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51
More things to try
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52
Summary
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53
Quiz on Detecting Pneumonia from Chest X-rays
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54
Understanding the Dataset
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55
Understanding the Problem Statement
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56
Setting up the Environment
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57
Processing the Image Data
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58
Applying Data Augmentation
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59
Trying Different Models
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60
Evaluating Model on the Test Data
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61
Real Time Prediction using CNN Models
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62
Summary
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63
Quiz on Fruits Identification System
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64
Understanding the Stock Market
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65
Understanding the problem Statement
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66
Setting up the Environment
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67
Fetching the Stock Market Data
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68
Understanding the Stock Market Data
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69
Understanding the Trends within the Data
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70
Processing the stock Market Data
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71
Forecasting with LSTMs
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72
Visualizing predictions
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73
Scraping Extra Features for Modelling
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74
Re-Training the LSTMs
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75
Possible Improvements
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76
Quiz on Stock Market Prediction
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77
Understanding the Problem Statement
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78
Understanding the Dataset
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79
Setting up the Environment
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80
Parsing Image Dataset
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81
Loading and Augmenting Image Data
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82
Training the Model
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83
Evaluating Model and Saving Objects
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84
Setting up local environment
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85
Using Tensorflow and OpenCV realtime prediction (Part - 1)
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86
Using Tensorflow and OpenCV realtime prediction (Part - 2)
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87
Project Summary
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88
Quiz on Face Expression Recognizer
