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1
Introduction
2
Selfdriving cars
3
Selfdriving toy car
4
Deep learning
5
Outline
6
Pipeline
7
DonkeyCar
8
Data collection
9
Getting data
10
Data collection pipeline
11
Data points
12
Data processing
13
Data splitting
14
Motivation
15
Tensorflow records
16
Serialization
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sterilized mode
18
pictures
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deep layers
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input
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convolution
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max pooling
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dense layer
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dropout layer
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training
26
training script
27
evaluation
28
metrics
29
measurement
30
typical mistakes
31
deployment
32
summary
33
driving errors
34
all software projects
35
tracking results
36
running the model
37
data gathering
38
building a selfdriving car
39
level 5 selfdriving cars
40
validation and test dates
41
why Tensorflow
42
how many people
43
simulation
Description:
Explore an end-to-end deep learning pipeline for self-driving cars in this 44-minute conference talk. Dive into the world of autonomous vehicles, starting with a toy car implementation and progressing through data collection, processing, and model training using TensorFlow. Learn about the pipeline components, including DonkeyCar, data serialization, and neural network architecture. Discover common pitfalls, evaluation metrics, and deployment strategies. Gain insights into scaling up to Level 5 autonomous vehicles and the importance of simulation in development. Understand the challenges of building self-driving cars and how to track results effectively in software projects.

End-to-End Deep Learning Pipeline for Self-Driving Cars

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Introduction