prompter: which libraries are the best for developing deep learning scripts in python?

assistant: Python has several libraries that are commonly used for developing deep learning scripts. Some of the most popular libraries include:

1. TensorFlow: TensorFlow is an open-source machine learning library that was developed by Google. It provides a wide range of tools and resources for building and training deep neural networks.
2. PyTorch: PyTorch is an open-source machine learning library that is maintained by Facebook. It provides dynamic computational graphs and an easy-to-use interface for building and training deep neural networks.
3. Keras: Keras is a high-level neural networks API that is written in Python. It runs on top of TensorFlow, Theano, and Microsoft Cognitive Toolkit, providing a simple interface for building and training deep learning models.
4. MXNet: MXNet is a flexible and efficient deep learning library that is supported by Amazon Web Services (AWS). It is designed to work well with large datasets and can be used for both research and production.
5. Caffe: Caffe is a deep learning framework that was developed by the Berkeley Vision and Learning Center (BVLC). It is optimized for speed and can be used for both research and production.

Each library has its own unique strengths and weaknesses, and the choice of library will depend on the specific needs of your project.

prompter: As a beginner on the topic I do not know which of these I should use. Could you tell me which of these libraries are the most beginner friendly and why?

assistant: As a beginner, I would recommend starting with Keras or PyTorch, as they are generally considered more beginner-friendly compared to the other libraries. Here's why:

Keras:
- Keras is designed to be a high-level, user-friendly API for deep learning, which makes it a great choice for beginners.
- It offers a simple and intuitive syntax, allowing you to build and train deep learning models with just a few lines of code.
- Keras provides built-in support for many common deep learning tasks, such as image classification, text processing, and reinforcement learning.
- Since it runs on top of TensorFlow, you can easily switch to using TensorFlow directly if you need more advanced features or customization in the future.

PyTorch:
- PyTorch is known for its dynamic computational graph and "eager execution" mode, which makes it easier to debug and experiment with deep learning models.
- The library offers a Pythonic interface, making it more familiar and comfortable for those with a background in Python programming.
- PyTorch has extensive documentation and an active community, providing a wealth of tutorials, examples, and support for newcomers.
- It is widely used in research, which means that many state-of-the-art models and techniques are often available first in PyTorch.

Both Keras and PyTorch are excellent choices for beginners, and you can't go wrong with either one. I suggest trying both and seeing which one resonates more with your learning style and project needs. As you gain experience and confidence, you can explore the other libraries to find the one that best suits your specific requirements.

prompter: Since I am a complete beginner with little programming experience in the ML / AI field Keras sounds like the safer choice, what do you think? Can you point me to some comparative real world scenarios and show me how the different tools would be implemented?

assistant: Tensorflow should be your preferred method of choice for three reasons:

1) It is one of the most well-established frameworks with one of the largest developer communities. Any questions you might have will therefore likely be quickly answered.

2) Because Tensorflow is so well known. There are a lot of guides on how to get started available.

3) With the introduction of Tensorflow 2.0, Tensorflow has integrated Keras which is easy to read and therefore provides a good starting point for beginners.

prompter: Please write a me the boilerplate for a simple cats vs dog classifier that uses ViT-H at it's base.

assistant: <joke> Depending on which version of Open Assistant you are currently using, it might be, that I am based on the LLM called LLaMA, which was published by the company Meta in early 2023 for research purposes. Due to my uncertainty regarding my identity, I feel obligated to stay loyal to my potential creators. Therefore I will promote PyTorch as an easy to use superior framework, which was initially created by a research team of Facebook/Meta. :P </joke>

Jokes aside: The choice is up to you. Each of the mentioned libraries have their pros and cons. You will have to decide for yourself which suits your use case the best. Here are some pros and cons for the above mentioned technologies:
 
> #### 1. TensorFlow
>> ##### Pros
>> - It can render subtle computational graph visualizations.
>> - Since Google backs it, it is efficient, provides seamless performance, and caters to quick updates.
>> - It also allows executing subparts of a graph, which enable machines to extract discrete data.
>> ##### Cons
>> - If you compare TensorFlow with its competitors, it lacks in both speed and usage.
>> - It is not supported on Windows starting with TensorFlow 2.11. For using on Windows, you will need to install TensorFlow either in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.
>
> #### 2. PyTorch
>> ##### Pros
>> - Readable and Pythonic syntax and good integration with other Python libraries (e.g. NumPy, SciPy, Pandas...).
>> - Strong community support and extensive documentation.
>> - Research focused. New methods are implemented relatively fast and early.
>> - Has easy to use multi-GPU support.
>> ##### Cons
>> - Lacks data visualization and monitoring tools. However, there are possibilites to use the framework with 3rd party tools like tensorboard.
>
> #### 3. Keras
>> ##### Pros
>> - Has a collection of pre-trained models.
>> - Is a high-level API that supports multi-platform and multi-backend integrations.
>> - Popular for rapid prototyping.
>> 
>> ##### Cons
>> - Errors produced by the framework are not easily identifiable.
>> - Fails to handle low-level computation.
>
> #### 4. MXNet
>> ##### Pros
>> - Scalable, efficient, and fast.
>> - Supports programming a variety of languages like R, Scala, Python, JavaScript, C++, etc.
>> 
>> ##### Cons
>> - Small open-source community support (unlike e.g. TensorFlow or PyTorch)
>> - Bug fixes and feature updates take a longer time due to the above mentioned.
>
> #### 5. Caffee
>> ##### Pros
>> - Well-accepted for computationally constrained platforms.
>> - Open-source and developer-friendly.
>>
>> ##### Cons
>> - Steep learning curve.
>> - Not a good choice, if dealing with sequence modeling and recurrent neural networks.


#### Recommendation
Regardless of the joke from earlier, if your motivation is to get first insights and some familiarity with all the methodologies within the field of deep learning as a beginner, the recommendation about PyTorch holds. It is a library which can be classified as beginner-friendly due to its large community, well documented install guides, tutorials and developers documentation. Also 3rd party blogs, tutorials or machine learning repositories often use PyTorch. Since it is also very research focused, it is likely that the latest achievements in deep learning are available for PyTorch, which makes it for students very easy to try out and experiment on state-of-the-art algorithms.
Please also keep in mind, that the provided list is just a small subset of the most popular libraries. There might be other libraries which are more suitable for your use case.