OpenAI

Pros

• High accuracy for a variety of tasks
• Easy to use and get started with
• Reach wide and diverse range of applications
• Can create completely custom training routines

Cons

• Limited deployment options
• Requires extensive knowledge of the underlying code to modify or customize
• Can be a bit slow and consuming regarding resources

Teachable Machine

Pros

• Web-based environment
• Easy drag & drop customization
• Versatile set of tools to choose from
• Runs on all types of hardware

Cons

• Limited documentation
• Limited transfer learning capabilities
• Easy to get carried away with customization
• No support for deployment

Scikit-learn

Pros

• Very simple interface
• Supports several different machine learning models
• Easy to implement and customize
• Can work with very large datasets

Cons

• Limited transfer learning capabilities
• Poorly documented
• Poor visualization library
• Limited support for deep learning

TensorFlow

Pros

• Supports a wide variety of commonly used deep learning models
• Extremely large online community
• Easy to maintain code
• Versatile deployment options

Cons

• Poorly documented API
• Unintuitive to new users
• Resource intensive
• Requires extensive knowledge to create custom models

Keras

Pros

• Very user friendly API
• Built-in support for transfer learning
• Easy to maintain code
• High accuracy for a variety of tasks

Cons

• Limited support for custom models
• Limited deployment options
• Poorly documented API
• Limited visualization capabilities

PyTorch

Pros

• Variable length sequences support
• Very intuitive API
• Graph-based execution platform
• Built-in support for deployment

Cons

• Poorly documented API
• Performance is worse on smaller datasets
• Limited transfer learning capabilities
• Limited support for custom models

AutoKeras

Pros

• Easy to use and get started with
• Automates most of the tedious parts of machine learning
• High accuracy on a variety of tasks
• Supports most types of data

Cons

• Limited control over the model and parameters
• Limited transfer learning capabilities
• Not suitable for more complex tasks
• Poorly documented API

H2O.ai

Pros

• Easy to use and get started with
• Automates most of the tedious parts of machine learning
• Good scalability for large datasets
• Very user friendly API

Cons

• Limited transfer learning capabilities
• Limited control over the model and parameters
• Poorly documented API
• Not suitable for complex tasks