Is Google Gemini better for certain kinds of tasks?

OutScripts allow you to skim, review and easily search across long videos.

See an example OutScript here which is based on the first lesson in the “Deep Learning for Coders” YouTube course.

Google Gemini is a much better choice than other LLMs for creating OutScripts because you can use it as a part of Google AI Studio, and Google AI Studio is free for up to two requests per minute, while GPT4 does not have a free equivalent, and GPT3 is not able to handle the size of the prompt.

It provides better summaries for the same prompt compared to Claude.

This is Claude’s summary:

There appears to be only one main speaker in this transcript:
Speaker 1: Jeremy Howard (the instructor)
Summary with timestamps:
0:02 | Introduction to Practical Deep Learning for Coders
1:08 | Demonstration of building a bird recognition model
5:00 | Overview of recent advances in deep learning
10:25 | Introduction to Jupyter notebooks and data handling
22:19 | Explanation of data blocks and learners in fast.ai
29:35 | Demonstration of image segmentation
33:39 | Overview of tabular analysis and collaborative filtering
42:56 | Discussion on the current capabilities of deep learning
49:51 | Explanation of how the presentation is made using RISE
52:33 | Overview of various applications of Jupyter notebooks
1:08:00 | Discussion on the current state and potential of deep learning
1:11:11 | Explanation of the basic principles of machine learning
1:18:21 | Wrap-up and homework assignment

And this is Google Gemini’s summary:

0:02 | Welcome and Introduction
0:22 | Deep Learning in 2015 vs. Today
1:15 | Building a Bird Recognizer
2:08 | Images as Numbers
3:24 | Data Collection and Preparation
4:26 | Data Block
5:17 | Training the Model
7:13 | Recent Advancements in Deep Learning
11:33 | Ethical Considerations
12:16 | Learning Approach and Feedback
14:25 | Shoutout to Radic
15:29 | Importance of Context in Learning
17:51 | About Jeremy Howard
18:08 | Deep Learning for Coders Book
22:29 | Why Deep Learning is Now Possible
23:05 | Classic Machine Learning Approach
24:07 | Neural Networks and Feature Learning
26:31 | Deep Learning's Automatic Feature Extraction
27:02 | Image-Based Algorithms and Their Applications
28:34 | Deep Learning Doesn't Require Lots of Math, Data, or Expensive Computers
29:51 | Transfer Learning
30:04 | PyTorch and Fast.ai
31:31 | PyTorch vs. Fast.ai
33:22 | Fast.ai Library and its Recognition
33:41 | Jupyter Notebooks
34:31 | Cloud Servers and Kaggle
35:19 | Jupyter Notebook Example
38:26 | Bird or Not Bird Model in Jupyter Notebook
41:45 | Data Block Command
42:00 | Importance of Data Handling
43:12 | Practical Deep Learning Focus
44:05 | Data Block Components
47:08 | Data Loaders
48:25 | Documentation and Resources
49:17 | Learner Object
50:05 | Model Selection
51:40 | Pre-trained Models and Fine-tuning
53:35 | Deploying the Model
54:38 | Other Deep Learning Models
55:04 | Segmentation
56:49 | Data Loaders for Segmentation
58:19 | Tabular Analysis
59:06 | Data Sets in Fast.ai
1:00:05 | Tabular Data Loaders
1:01:14 | Collaborative Filtering
1:02:22 | Collaborative Filtering Data Loaders
1:03:11 | Collaborative Filtering Learner
1:04:51 | RISE for Jupyter Notebook Presentations
1:05:40 | Jupyter Notebook Applications
1:07:55 | Deep Learning's Capabilities
1:08:39 | Deep Learning's Impact Across Fields
1:10:21 | Deep Learning's History
1:11:11 | Machine Learning Basics
1:12:08 | Machine Learning Model vs. Traditional Program
1:13:27 | Neural Networks
1:14:49 | Training Process and Loss Function
1:15:49 | Neural Networks as Infinitely Flexible Functions
1:16:59 | Deploying a Trained Model
1:17:48 | Trained Model as a Code Component
1:18:20 | Experimentation and Resources
1:19:48 | Homework and Sharing Your Work
1:20:13 | Student Projects and Success Stories
1:22:14 | Conclusion and Encouragement

Not only is Gemini’s summary much more detailed, but it is also better aligned with the actual timestamps.

For example, the timestamp for “5:00 | Overview of recent advances in deep learning” is actually much better synchronized with the transcript if it starts after minute 7, as suggested by Gemini “7:13 | Recent Advancements in Deep Learning”