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Tensorflow for Practitioners with Python

Computers are getting smarter, and with AI pushed in to the pot, machine learning has become a prominent technological revolution that is changing how we run our devices. Devising algorithms for AI ar...

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  • English

Course Description

Computers are getting smarter, and with AI pushed in to the pot, machine learning has become a prominent technological revolution that is changing how we run our devices. Devising algorithms for AI aren’t that easy, and require an extensive library to help them perform various tasks. TensorFlow is one such library, this open-source library is created for dataflow programming across a range of t...

Computers are getting smarter, and with AI pushed in to the pot, machine learning has become a prominent technological revolution that is changing how we run our devices. Devising algorithms for AI aren’t that easy, and require an extensive library to help them perform various tasks. TensorFlow is one such library, this open-source library is created for dataflow programming across a range of tasks. It is also a symbolic math library that is commonly used for machine learning applications such as neural networks.

What you’ll learn
  • Introduction to TensorFlow
  • What is TensorFlow &why should you use it?
  • TensorFlow as an Interface and as an environment
  • Installing Tensorflow and becoming familiar with the interface
  • Running your first TensorFlow program
  • Building actual Neural Networks using TensorFlow
  • Deepening the Networks and integrating Deep Learning
  • Transfer Learning using Keras and TFLearn

Covering Topics

1
Section 1 : Introducting Tensorflow
2
Section 2 : Building Neural Networks using Tensorflow
3
Section 3 : Deep Learning using Tensorflow
4
Section 4 : Transfer Learning using Keras and TFLearn

Curriculum

      Section 1 : Introducting Tensorflow
1
Introducting Tensorflow Preview
2
Why Tensorflow?
3
What is tensorflow?
4
Tensorflow as an Interface
5
Tensorflow as an environment
6
Tensors Preview
7
Computation Graph
8
Skills Checklist
9
Modules Covered
10
Installing Tensorflow
11
Tensorflow training Preview
12
Prepare Data
13
Tensor types
14
Loss and Optimization
15
Running your first tensorflow program
      Section 2 : Building Neural Networks using Tensorflow
16
Back to tensors
17
Tensorflow data types
18
CPU vs GPU vs TPU
19
Tensorflow methods Preview
20
Introduction to Neural Networks
21
Neural Network Architecture
22
Linear Regression example revisited
23
The Neuron
24
Neural Network Layers Preview
25
The MNIST Dataset
26
Coding MNIST NN Demo
27
Summary
      Section 3 : Deep Learning using Tensorflow
28
Deepening the network
29
Images and Pixels
30
How humans recognise images
31
Convolutional Neural Networks
32
ConvNet Architecture
33
Overfitting and Regularization
34
Max Pooling and ReLU activations
35
Dropout
36
Strides and Zero Padding
37
Coding Deep ConvNets demo
38
Debugging Neural Networks
39
Visualising NN using Tensorflow
40
Tensorboard continued
41
Summary
      Section 4 : Transfer Learning using Keras and TFLearn
42
Transfer Learning Introduction
43
Google Inception Model
44
Retraining Google Inception with our own data demo
45
Predicting new images
46
Transfer Learning Summary
47
Extending Tensorflow
48
Keras Demo
49
TFLearn Demo
50
Keras vs TFLearn Comparison
51
Summary and Conclusion

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