English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 22 Hours | 4.59 GB
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go – a game where intuition plays a key role.
But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence.
What Will I Learn?
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
Table of Contents
Welcome to the course
1 What is Deep Learning
2 Updates on Udemy Reviews
3 BONUS Learning Paths
4 BONUS Meet Your Instructors
5 Some Additional Resources!!
6 FAQBot!
7 Get the materials
8 Your Shortcut To Becoming A Better Data Scientist!
Part 1 – Artificial Neural Networks ———————
9 Welcome to Part 1 – Artificial Neural Networks
ANN Intuition
10 What You’ll Need for ANN
11 Plan of Attack
12 The Neuron
13 The Activation Function
14 How do Neural Networks work
15 How do Neural Networks learn
16 Gradient Descent
17 Stochastic Gradient Descent
18 Backpropagation
Building an ANN
19 Business Problem Description
20 IMPORTANT NOTE
21 Building an ANN – Step 1
22 Check out our free course on ANN for Regression
23 Building an ANN – Step 2
24 Building an ANN – Step 3
25 Building an ANN – Step 4
26 Building an ANN – Step 5
Part 2 – Convolutional Neural Networks ——————–
27 Welcome to Part 2 – Convolutional Neural Networks
CNN Intuition
28 What You’ll Need for CNN
29 Plan of attack
30 What are convolutional neural networks
31 Step 1 – Convolution Operation
32 Step 1(b) – ReLU Layer
33 Step 2 – Pooling
34 Step 3 – Flattening
35 Step 4 – Full Connection
36 Summary
37 Softmax & Cross-Entropy
Building a CNN
38 IMPORTANT NOTE
39 Building a CNN – Step 1
40 Building a CNN – Step 2
41 Building a CNN – Step 3
42 Building a CNN – Step 4
43 Building a CNN – Step 5
44 Building a CNN – FINAL DEMO!
Part 3 – Recurrent Neural Networks ———————-
45 Welcome to Part 3 – Recurrent Neural Networks
RNN Intuition
46 What You’ll Need for RNN
47 Plan of attack
48 The idea behind Recurrent Neural Networks
49 The Vanishing Gradient Problem
50 LSTMs
51 Practical intuition
52 EXTRA LSTM Variations
Building a RNN
53 IMPORTANT NOTE
54 Building a RNN – Step 1
55 Building a RNN – Step 2
56 Building a RNN – Step 3
57 Building a RNN – Step 4
58 Building a RNN – Step 5
59 Building a RNN – Step 6
60 Building a RNN – Step 7
61 Building a RNN – Step 8
62 Building a RNN – Step 9
63 Building a RNN – Step 10
64 Building a RNN – Step 11
65 Building a RNN – Step 12
66 Building a RNN – Step 13
67 Building a RNN – Step 14
68 Building a RNN – Step 15
Evaluating and Improving the RNN
69 Evaluating the RNN
70 Improving the RNN
Part 4 – Self Organizing Maps ————————
71 Welcome to Part 4 – Self Organizing Maps
SOMs Intuition
72 Plan of attack
73 How do Self-Organizing Maps Work
74 Why revisit K-Means
75 K-Means Clustering (Refresher)
76 How do Self-Organizing Maps Learn (Part 1)
77 How do Self-Organizing Maps Learn (Part 2)
78 Live SOM example
79 Reading an Advanced SOM
80 EXTRA K-means Clustering (part 2)
81 EXTRA K-means Clustering (part 3)
Building a SOM
82 IMPORTANT NOTE
83 How to get the dataset
84 Building a SOM – Step 1
85 Building a SOM – Step 2
86 Building a SOM – Step 3
87 Building a SOM – Step 4
Mega Case Study
88 IMPORTANT NOTE
89 Mega Case Study – Step 1
90 Mega Case Study – Step 2
91 Mega Case Study – Step 3
92 Mega Case Study – Step 4
Part 5 – Boltzmann Machines ————————-
93 Welcome to Part 5 – Boltzmann Machines
94 Plan of attack
Boltzmann Machine Intuition
95 Boltzmann Machine
96 Energy-Based Models (EBM)
97 Editing Wikipedia – Our Contribution to the World
98 Restricted Boltzmann Machine
99 Contrastive Divergence
100 Deep Belief Networks
101 Deep Boltzmann Machines
102 How to get the dataset
Building a Boltzmann Machine
103 IMPORTANT NOTE
104 Installing PyTorch
105 Building a Boltzmann Machine – Introduction
106 Same Data Preprocessing in Parts 5 and 6
107 Building a Boltzmann Machine – Step 1
108 Building a Boltzmann Machine – Step 2
109 Building a Boltzmann Machine – Step 3
110 Building a Boltzmann Machine – Step 4
111 Building a Boltzmann Machine – Step 5
112 Building a Boltzmann Machine – Step 6
113 Building a Boltzmann Machine – Step 7
114 Building a Boltzmann Machine – Step 8
115 Building a Boltzmann Machine – Step 9
116 Building a Boltzmann Machine – Step 10
117 Building a Boltzmann Machine – Step 11
118 Building a Boltzmann Machine – Step 12
119 Building a Boltzmann Machine – Step 13
120 Building a Boltzmann Machine – Step 14
121 Evaluating the Boltzmann Machine
Part 6 – AutoEncoders —————————-
122 Welcome to Part 6 – AutoEncoders
123 Plan of attack
AutoEncoders Intuition
124 Auto Encoders
125 A Note on Biases
126 Training an Auto Encoder
127 Overcomplete hidden layers
128 Sparse Autoencoders
129 Denoising Autoencoders
130 Contractive Autoencoders
131 Stacked Autoencoders
132 Deep Autoencoders
Building an AutoEncoder
133 IMPORTANT NOTE
134 How to get the dataset
135 Installing PyTorch
136 Same Data Preprocessing in Parts 5 and 6
137 Building an AutoEncoder – Step 1
138 Building an AutoEncoder – Step 2
139 Building an AutoEncoder – Step 3
140 Homework Challenge – Coding Exercise
141 Building an AutoEncoder – Step 4
142 Building an AutoEncoder – Step 5
143 Building an AutoEncoder – Step 6
144 Building an AutoEncoder – Step 7
145 Building an AutoEncoder – Step 8
146 Building an AutoEncoder – Step 9
147 Building an AutoEncoder – Step 10
148 Building an AutoEncoder – Step 11
149 THANK YOU bonus video
Annex – Get the Machine Learning Basics ——————-
150 Annex – Get the Machine Learning Basics
Regression & Classification Intuition
151 What You Need for Regression & Classification
152 Simple Linear Regression Intuition – Step 1
153 Simple Linear Regression Intuition – Step 2
154 Multiple Linear Regression Intuition
155 Logistic Regression Intuition
Data Preprocessing Template
156 Important Instructions
157 Data Preprocessing – Step 1
158 Data Preprocessing – Step 2
159 Data Preprocessing – Step 3
160 Data Preprocessing – Step 4
161 Data Preprocessing – Step 5
162 Data Preprocessing – Step 6
163 Data Preprocessing – Step 7
Logistic Regression Implementation
164 Important Instructions
165 Logistic Regression – Step 1
166 Logistic Regression – Step 2
167 Logistic Regression – Step 3
168 Logistic Regression – Step 4
169 Logistic Regression – Step 5
170 Logistic Regression – Step 6
171 Logistic Regression – Step 7
Bonus Lectures
172 YOUR SPECIAL BONUS
Resolve the captcha to access the links!