Deep learning is appropriate for analyzing large amounts of complex data like images, audio, and text. It works by building models with many layers that learn representations of data. The document provides an overview of deep learning concepts like neural networks, convolutions, activations, and frameworks like Keras. It also discusses when deep learning is suitable and companies that offer deep learning services.

1. Deep Learning For Developers
Ben Taylor

2. When I’m Not Doing Data Science

3. My Path To Data Scientist

5. HIREVUE | TALENT INTERACTION
Raw Audio Analysis
The raw audio utterance data can be used to create metrics (i.e. utterance/gap
ratio, median utterance, max intra-utterance gap, etc…) and it can be analyzed
further to perform spectral analysis on each utterance and even pull out utterance
repetition metrics.

6. HIREVUE | TALENT INTERACTION
YesNo
What you say matters

8. Deep Learning For Devs
What is deep learning?
When is deep learning appropriate?
Who services deep learning?
Deep learning 101
Image
Audio
Text

9. What Is Deep Learning?

10. What Is Deep Learning?
Super human status post 2015

11. Building Blocks: Convolution
SMA = [ 1,1,1,1]
EWMA = [ 0.1, 0.2, 0.4, 0.9]
OTHER = [0.1, 10, 10, 0.1]

12. Building Blocks: Convolution

13. Building Blocks: Neural Network
(x1)(w1)+(x2)(w2)+(x3)(w3) = out

14. Building Blocks: Neural Network
Now Imagine something 100M
times bigger
(brain is 1T times)

15. Building Blocks: Activation

16. Building Blocks: Activation
Activation

17. Feature Ensembles

18. Convolution Deep Belief Network
Micro
Macro

19. Convolution Deep Belief Network

20. Keras Code
Building deep learning models is easy….
getting them to converge….

21. When Is Deep Learning Appropriate?
Deep learning

22. Who services deep learning?

23. Deep Learning 101

24. Why We Like Removing Mean From Inputs