The document outlines an agenda for a Tensorflow basics workshop. It includes an opening speech on Tesla AI, an introduction to key concepts like neural networks and machine learning workflows. The bulk of the workshop involves coding sessions where participants will build a classification model in Tensorflow and get help from instructors. It concludes with information on continuing self-directed learning through online resources and a preview of an upcoming computer vision lesson.

1. Tensorflow Basics
Speaker: Lily wang, Sam Chen
2022/10/13 18:30~20:50

2. 6:30~6:35 Opening speech: Future of driving - Tesla
6:35~6:50 Roadmap and workflow of data science
6:50~7:20 Introduce toolkits & learning materials
7:20~7:30 Break
7:30~8:20 Coding session (build classification model)
8:20~8:30 Q&A
Agenda
Deep learning basics

3. Opening Speech:
Tesla AI Day
2022

4. How to solve
the problem
What is AI,
ML & DL

5. Roadmap & workflow
of data science

6. Introduce
toolkits & learning materials

7. Tensorflow: the
best tools to
learn DL
Machine learning education | TensorFlow
Tutorials | TensorFlow Core

8. How can I self-directed learning
coding?

9. How can I self-directed learning
coding?
TensorFlow 2.0 Complete
Course - Python Neural
Networks for Beginners
Tutorial
TensorFlow 2.9
documentation — DevDocs
GitHub - tensorflow/docs:
TensorFlow documentation

10. Coding session
(build classification model)
Now it’s time to start this adventure.
Have you ever code?
If so, what’s project are you working on
If not, what kind of topic you are interested?

11. Algorithm: Problem-solving
Problem: Find team members for Google solution challenge
Inputs: Previous programing experiences, skills, interests
Outputs: Choice for different roles
Input Algorithm Output

12. Machine learning

13. Recommended workflow
● Data acquisition, annotation(supervised learning)
● Problem cases: regression(output:1), classification(output:>1)
● Data preprocessing (augmentation, adjustment)
● Feature extraction (find possible key component behind)
● Build model (parameters, train-test split)
● Result evaluation (confusion matrix, accuracy)
● Make prediction (compare with ground truth)

14. Neurons vs Neural network(NN)
Input
output

15. Problem cases
Regression: weather forecast,
probability of precipitation (%)
Classification: decisions (yes
or no), species (cat, dog, cow)

16. Activation function
Let’s Code

17. Epochs vs batch size
Issue: Memory cannot afford large input data
Iterations = (Data size/ batch size) * Epochs
● Iterations: Loop number implemented by programers
● Batch size: Divide data into several sections
● Epochs: How many times do the algorithm read through the
whole dataset

18. Google Colab
● Programing language: Python (jupyter notebook)
● Up-to-date environment and mainly data science toolkit installed
● High compute speed (FREE GPU/TPU for specific amount daily)
● Used for prototyping, testing, demonstrating

19. Confusion matrix

20. Data analysis

21. 10/27 Computer vision lesson
● Data preprocessing: adjustment, augmentation
● Transfer learning, pretrained-weight
● Model for CV: CNN, Yolo
● Application: object detection, classification, segmentation, art

22. If you have any further questions please contact
your regional point of contact.
Thanks for listening
Now it’s time to start this adventure.