English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 10 Hours | 1.83 GB
Understand and build Deep Learning models for images, text and more using Python and Keras
This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.
We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems.
Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.
This course is a good balance between theory and practice. We don’t shy away from explaining mathematical details and at the same time we provide exercises and sample code to apply what you’ve just learned.
The goal is to provide students with a strong foundation, not just theory, not just scripting, but both. At the end of the course you’ll be able to recognize which problems can be solved with Deep Learning, you’ll be able to design and train a variety of Neural Network models and you’ll be able to use cloud computing to speed up training and improve your model’s performance.
What you’ll learn
- To describe what Deep Learning is in a simple yet accurate way
- To explain how deep learning can be used to build predictive models
- To distinguish which practical applications can benefit from deep learning
- To install and use Python and Keras to build deep learning models
- To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
- To build, train and use fully connected, convolutional and recurrent neural networks
- To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
- To train and run models in the cloud using a GPU
- To estimate training costs for large models
- To re-use pre-trained models to shortcut training time and cost (transfer learning)
Table of Contents
Welcome to the course!
1 Welcome to the course!
2 Introduction
3 Real world applications of deep learning
4 Download and install Anaconda
5 Installation Video Guide
6 Obtain the code for the course
7 Course Folder Walkthrough
8 Your first deep learning model
Data
9 Section 2 Intro
10 Exercise 1 Solution
11 Exercise 2 Presentation
12 Exercise 2 Solution
13 Exercise 3 Presentation
14 Exercise 3 Solution
15 Exercise 4 Presentation
16 Exercise 4 Solution
17 Exercise 5 Presentation
18 Exercise 5 Solution
19 Tabular data
20 Data exploration with Pandas code along
21 Visual data Exploration
22 Plotting with Matplotlib
23 Unstructured Data
24 Images and Sound in Jupyter
25 Feature Engineering
26 Exercise 1 Presentation
Machine Learning
27 Section 3 Intro
28 Evaluating Performance code along
29 Classification
30 Classification code along
31 Overfitting
32 Cross Validation
33 Cross Validation code along
34 Confusion matrix
35 Confusion Matrix code along
36 Feature Preprocessing code along
37 Exercise 1 Presentation
38 Machine Learning Problems
39 Exercise 1 solution
40 Exercise 2 Presentation
41 Exercise 2 solution
42 Supervised Learning
43 Linear Regression
44 Cost Function
45 Cost Function code along
46 Finding the best model
47 Linear Regression code along
48 Evaluating Performance
Deep Learning Intro
49 Section 4 Intro
50 Exercise 1 Presentation
51 Exercise 1 Solution
52 Exercise 2 Presentation
53 Exercise 2 Solution
54 Exercise 3 Presentation
55 Exercise 3 Solution
56 Exercise 4 Presentation
57 Exercise 4 Solution
58 Deep Learning successes
59 Neural Networks
60 Deeper Networks
61 Neural Networks code along
62 Multiple Outputs
63 Multiclass classification code along
64 Activation Functions
65 Feed forward
Gradient Descent
66 Section 5 Intro
67 Learning Rate code along
68 Gradient Descent
69 Gradient Descent code along
70 EWMA
71 Optimizers
72 Optimizers code along
73 Initialization code along
74 Inner Layers Visualization code along
75 Exercise 1 Presentation
76 Exercise 1 Solution
77 Derivatives and Gradient
78 Exercise 2 Presentation
79 Exercise 2 Solution
80 Exercise 3 Presentation
81 Exercise 3 Solution
82 Exercise 4 Presentation
83 Exercise 4 Solution
84 Tensorboard
85 Backpropagation intuition
86 Chain Rule
87 Derivative Calculation
88 Fully Connected Backpropagation
89 Matrix Notation
90 Numpy Arrays code along
91 Learning Rate
Convolutional Neural Networks
92 Section 6 Intro
93 Convolution in 2 D
94 Image Filters code along
95 Convolutional Layers
96 Convolutional Layers code along
97 Pooling Layers
98 Pooling Layers code along
99 Convolutional Neural Networks
100 Convolutional Neural Networks code along
101 Weights in CNNs
102 Beyond Images
103 Features from Pixels
104 Exercise 1 Presentation
105 Exercise 1 Solution
106 Exercise 2 Presentation
107 Exercise 2 Solution
108 MNIST Classification
109 MNIST Classification code along
110 Beyond Pixels
111 Images as Tensors
112 Tensor Math code along
113 Convolution in 1 D
114 Convolution in 1 D code along
Cloud GPUs
115 Google Colaboratory GPU notebook setup
116 Floyd GPU notebook setup
Recurrent Neural Networks
117 Section 8 Intro
118 Exercise 1 Presentation
119 Exercise 1 Solution
120 Exercise 2 Presentation
121 Exercise 2 Solution
122 Time Series
123 Sequence problems
124 Vanilla RNN
125 LSTM and GRU
126 Time Series Forecasting code along
127 Time Series Forecasting with LSTM code along
128 Rolling Windows
129 Rolling Windows code along
Improving performance
130 Section 9 Intro
131 Image Generator code along
132 Hyperparameter search
133 Embeddings
134 Embeddings code along
135 Movies Reviews Sentiment Analysis code along
136 Exercise 1 Presentation
137 Exercise 1 Solution
138 Exercise 2 Presentation
139 Exercise 2 Solution
140 Exercise 3 Presentation
141 Learning curves
142 Learning curves code along
143 Batch Normalization
144 Batch Normalization code along
145 Dropout
146 Dropout and Regularization code along
147 Data Augmentation
148 Continuous Learning
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