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Deep Learning A-Z™: Hands-On Artificial Neural Networks

22h 36m 30s
English
Paid

Artificial intelligence is growing exponentially, impacting numerous fields and industries. From self-driving cars amassing millions of miles to IBM Watson diagnosing patients more accurately than teams of doctors, and Google DeepMind's AlphaGo defeating the world champion at Go—AI is reimagining the boundaries of technology.

Course Overview

As AI continues to advance, the complexity of the problems it addresses also increases. Deep Learning is at the heart of Artificial Intelligence, providing solutions to these complex challenges.

Why Choose Deep Learning A-Z™?

Discover five key reasons why Deep Learning A-Z™ stands out among other training programs:

  1. Robust Structure: The course is structured into two volumes - Supervised and Unsupervised Deep Learning, each focusing on three key algorithms, providing a comprehensive understanding of Deep Learning.
  2. Intuition Tutorials: Unlike other courses, we emphasize understanding the rationale behind Deep Learning algorithms, developing an intuitive grasp before delving into theory or coding.
  3. Exciting Projects: Work with Real-World datasets to solve actual business problems, avoiding outdated data sets. Projects include challenges in customer churn, image recognition, stock price predictions, fraud detection, and recommender systems.
  4. Hands-On Coding: Learn by coding from scratch with step-by-step guidance, making the course practical and directly applicable to your projects.
  5. In-Course Support: Access our team of Data Scientists ready to assist with any questions within 48 hours, ensuring continuous learning support.

Tools Covered

Gain proficiency in the most popular tools for Deep Learning:

Tensorflow and PyTorch

These open-source libraries are core to Deep Learning. You'll learn when to use each and how they compare, gaining practical experience in both.

Additional Tools

  • Theano: Similar to Tensorflow, it provides a powerful deep learning foundation.
  • Keras: A high-level API simplifying the implementation of Deep Learning models through concise code.
  • Scikit-learn: Essential for model evaluation, parameter tuning, and data preprocessing.

Enhance your Python skills using libraries such as Numpy, Matplotlib, and Pandas for comprehensive data manipulation and visualization.

Who Is This Course For?

This course is perfect for:

  • Beginners wanting an accessible introduction to Deep Learning without being overwhelmed by programming or math complexities.
  • Experienced individuals seeking to master cutting-edge Deep Learning algorithms and gain hands-on experience with real-world challenges.
  • Data analysts, students, business owners, or anyone interested in leveraging Deep Learning for their career or business.

Real-World Case Studies

Apply your knowledge in the following real-world scenarios:

  • Churn Modelling Problem: Predict customer attrition using Artificial Neural Networks.
  • Image Recognition: Build Convolutional Neural Networks to identify objects in images.
  • Stock Price Prediction: Utilize Recurrent Neural Networks for predicting stock market trends.
  • Fraud Detection: Detect fraudulent credit card applications using Unsupervised models.
  • Recommender Systems: Develop systems to suggest movies or products, improving customer experience.

Course Requirements

  • High school-level mathematics
  • Basic knowledge of Python programming

What You'll Learn

  • Understand and implement various Neural Networks, including Artificial, Convolutional, Recurrent, Self-Organizing Maps, Boltzmann Machines, and AutoEncoders.

About the Author: udemy

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#1: Updates on Udemy Reviews
All Course Lessons (155)
#Lesson TitleDurationAccess
1
Updates on Udemy Reviews Demo
02:58
2
What is Deep Learning?
12:35
3
Installing Python
07:28
4
How to get the dataset
01:33
5
Plan of Attack
02:53
6
The Neuron
16:16
7
The Activation Function
08:30
8
How do Neural Networks work?
12:49
9
How do Neural Networks learn?
13:00
10
Gradient Descent
10:14
11
Stochastic Gradient Descent
08:45
12
Backpropagation
05:23
13
How to get the dataset
01:33
14
Business Problem Description
05:00
15
Building an ANN - Step 1
12:41
16
Building an ANN - Step 2
17:17
17
Building an ANN - Step 3
03:15
18
Building an ANN - Step 4
02:22
19
Building an ANN - Step 5
12:21
20
Building an ANN - Step 6
02:44
21
Building an ANN - Step 7
03:33
22
Building an ANN - Step 8
06:56
23
Building an ANN - Step 9
06:22
24
Building an ANN - Step 10
06:54
25
Homework Solution
13:04
26
Evaluating the ANN
19:36
27
Improving the ANN
07:25
28
Tuning the ANN
19:41
29
Plan of attack
03:32
30
What are convolutional neural networks?
15:50
31
Step 1 - Convolution Operation
16:39
32
Step 1(b) - ReLU Layer
06:42
33
Step 2 - Pooling
14:14
34
Step 3 - Flattening
01:53
35
Step 4 - Full Connection
19:26
36
Summary
04:20
37
Softmax & Cross-Entropy
18:21
38
How to get the dataset
01:33
39
Introduction to CNNs
04:09
40
Building a CNN - Step 1
09:31
41
Building a CNN - Step 2
03:01
42
Building a CNN - Step 3
01:06
43
Building a CNN - Step 4
12:52
44
Building a CNN - Step 5
04:59
45
Building a CNN - Step 6
05:00
46
Building a CNN - Step 7
05:50
47
Building a CNN - Step 8
02:50
48
Building a CNN - Step 9
19:46
49
Building a CNN - Step 10
08:26
50
Homework Solution
16:05
51
Plan of attack
02:33
52
The idea behind Recurrent Neural Networks
16:03
53
The Vanishing Gradient Problem
14:28
54
LSTMs
19:48
55
Practical intuition
15:12
56
EXTRA: LSTM Variations
03:38
57
How to get the dataset
01:33
58
Building a RNN - Step 1
06:30
59
Building a RNN - Step 2
07:05
60
Building a RNN - Step 3
05:58
61
Building a RNN - Step 4
14:24
62
Building a RNN - Step 5
10:41
63
Building a RNN - Step 6
02:51
64
Building a RNN - Step 7
08:43
65
Building a RNN - Step 8
05:21
66
Building a RNN - Step 9
03:21
67
Building a RNN - Step 10
04:22
68
Building a RNN - Step 11
10:32
69
Building a RNN - Step 12
05:23
70
Building a RNN - Step 13
16:51
71
Building a RNN - Step 14
08:16
72
Building a RNN - Step 15
09:37
73
Plan of attack
03:11
74
How do Self-Organizing Maps Work?
08:31
75
Why revisit K-Means?
02:20
76
K-Means Clustering (Refresher)
14:18
77
How do Self-Organizing Maps Learn? (Part 1)
14:25
78
How do Self-Organizing Maps Learn? (Part 2)
09:38
79
Live SOM example
04:29
80
Reading an Advanced SOM
14:27
81
EXTRA: K-means Clustering (part 2)
07:49
82
EXTRA: K-means Clustering (part 3)
11:52
83
How to get the dataset
01:33
84
Building a SOM - Step 1
13:43
85
Building a SOM - Step 2
09:40
86
Building a SOM - Step 3
17:26
87
Building a SOM - Step 4
11:13
88
Mega Case Study - Step 1
02:50
89
Mega Case Study - Step 2
04:17
90
Mega Case Study - Step 3
14:38
91
Mega Case Study - Step 4
09:03
92
Plan of attack
02:25
93
Boltzmann Machine
14:23
94
Energy-Based Models (EBM)
10:40
95
Editing Wikipedia - Our Contribution to the World
03:29
96
Restricted Boltzmann Machine
17:30
97
Contrastive Divergence
16:29
98
Deep Belief Networks
05:24
99
Deep Boltzmann Machines
02:58
100
How to get the dataset
01:33
101
Building a Boltzmann Machine - Introduction
09:10
102
Building a Boltzmann Machine - Step 1
09:14
103
Building a Boltzmann Machine - Step 2
09:41
104
Building a Boltzmann Machine - Step 3
08:22
105
Building a Boltzmann Machine - Step 4
20:54
106
Building a Boltzmann Machine - Step 5
05:06
107
Building a Boltzmann Machine - Step 6
07:34
108
Building a Boltzmann Machine - Step 7
10:14
109
Building a Boltzmann Machine - Step 8
12:37
110
Building a Boltzmann Machine - Step 9
06:18
111
Building a Boltzmann Machine - Step 10
11:35
112
Building a Boltzmann Machine - Step 11
06:58
113
Building a Boltzmann Machine - Step 12
13:24
114
Building a Boltzmann Machine - Step 13
18:43
115
Building a Boltzmann Machine - Step 14
17:11
116
Plan of attack
02:13
117
Auto Encoders
10:51
118
A Note on Biases
01:16
119
Training an Auto Encoder
06:11
120
Overcomplete hidden layers
03:53
121
Sparse Autoencoders
06:16
122
Denoising Autoencoders
02:33
123
Contractive Autoencoders
02:24
124
Stacked Autoencoders
01:55
125
Deep Autoencoders
01:51
126
How to get the dataset
01:33
127
Building an AutoEncoder - Step 1
12:05
128
Building an AutoEncoder - Step 2
11:50
129
Building an AutoEncoder - Step 3
08:22
130
Building an AutoEncoder - Step 4
20:52
131
Building an AutoEncoder - Step 5
05:05
132
Building an AutoEncoder - Step 6
16:46
133
Building an AutoEncoder - Step 7
13:38
134
Building an AutoEncoder - Step 8
15:06
135
Building an AutoEncoder - Step 9
13:33
136
Building an AutoEncoder - Step 10
04:23
137
Building an AutoEncoder - Step 11
11:27
138
THANK YOU bonus video
02:41
139
Simple Linear Regression Intuition - Step 1
05:46
140
Simple Linear Regression Intuition - Step 2
03:10
141
Multiple Linear Regression Intuition
01:04
142
Logistic Regression Intuition
17:08
143
Data Preprocessing - Step 1
07:26
144
Data Preprocessing - Step 2
07:55
145
Data Preprocessing - Step 3
10:40
146
Data Preprocessing - Step 4
12:58
147
Data Preprocessing - Step 5
10:41
148
Data Preprocessing - Step 6
10:50
149
Data Preprocessing Template
03:42
150
Logistic Regression Implementation - Step 1
05:22
151
Logistic Regression Implementation - Step 2
03:22
152
Logistic Regression Implementation - Step 3
02:35
153
Logistic Regression Implementation - Step 4
04:14
154
Logistic Regression Implementation - Step 5
19:35
155
Classification Template
03:40
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