Here is a 2800+ word blog post outlining AI frameworks for beginners:
Artificial intelligence (AI) has transformed entire industries thanks to popular techniques like machine learning and deep learning. Central to many AI applications are frameworks that provide the underlying infrastructure to build, train and deploy intelligent systems.
With so many AI frameworks now available, it can be daunting for beginners to select one to learn and use. This guide provides an overview of the most popular AI frameworks for people just starting out in AI development.
What Are AI Frameworks and Why Are They Important?
AI frameworks are tools, libraries and services that allow developers and data scientists to more efficiently build AI applications. They provide solutions for managing data, training machine learning models, deploying predictions, and integrating AI into products.
Instead of data scientists and engineers needing to code models and infrastructure from scratch, frameworks give them pre-built capabilities so they can focus on high-value tasks. This accelerates AI development and makes the technology more accessible.
Leading technology companies like Google, Amazon, Microsoft and Facebook have all introduced their own frameworks. Other open source options also exist. We‘ll cover some of the top beginner-friendly choices below.
Top AI Frameworks for Beginners
Scikit-Learn
Scikit-learn is one of the most popular introductory machine learning libraries. Written in Python, it features a wide range of supervised and unsupervised learning algorithms including classification, regression and clustering.
Key Features:
- Open source, commercially usable
- Built on top of Python scientific computing packages NumPy, SciPy and matplotlib
- Consistent syntax and APIs across models makes it easy to learn
- Access to metrics for model evaluation and optimization
- Thorough documentation and examples make it beginner-friendly
Scikit-learn is a general framework that can be used to get started with machine learning. It may not scale to larger projects, but its simplicity makes it a top choice for novices in AI development.
TensorFlow
Originally created by Google, TensorFlow has become one of the most widely used machine learning frameworks. It is designed to efficiently perform the complex mathematical operations involved in neural networks.
Key Features:
- Open source with support from Google Cloud
- Uses data flow graphs to represent calculations, models and data
- Modular architecture makes it flexible to deploy on different platforms
- Broad capabilities spanning computer vision, NLP, embeddings and more
- Simpler Keras API sits on top of TensorFlow for easier model building
TensorFlow provides a rich set of tools for beginners all the way up to advanced researchers. The Keras integration makes defining and training neural network models more straightforward so it can be less daunting to start with.
Pytorch
Developed by Facebook, PyTorch is another hugely popular open source machine learning library based on the Python language. It is regarded as more pythonic than frameworks written in other languages like C++ or Java.
Key Features:
- Open source with strong corporate support from Facebook and Microsoft
- Utilizes tensors and dynamical neural networks for eager yet efficient computations
- Integrates well with Python data science stacks thanks to NumPy underpinnings
- More like Python programming, enabling easier debugging than graph paradigm
- Modular, composable structure allows mixing pre-built and custom components
A major advantage of PyTorch for beginners is its imperitive programming model. This makes developing and debugging machine learning code closer to how you would write regular Python code. It helps ease the learning curve for coding AI models.
Specialty AI Frameworks
In addition to the most popular general machine learning frameworks above, there are more specialized frameworks tailored for particular domains or tasks:
- Computer Vision: OpenCV, SimpleCV
- Natural Language Processing (NLP): NLTK, spaCy, AllenNLP
- Reinforcement Learning: OpenAI Gym, Coach
- Robotics: ROS (Robot Operating System)
- Edge/Embedded Devices: TensorFlow Lite, Core ML
These frameworks offer more targeted tooling and capabilities if you‘re looking to focus on a specific AI field from the start.
Cloud AI Services
The major cloud platforms from Amazon, Microsoft and Google now all offer managed artificial intelligence services. These provide pre-built capabilities so you don‘t need to piece together your own frameworks and infrastructure.
Some top examples include:
Amazon Web Services (AWS)
- SageMaker – Machine learning model building, training and deployment
- Rekognition – Image and video analysis
- Comprehend – Natural language processing
- Lex – Conversational bots
Microsoft Azure
- Machine Learning Service – Drag-and-drop model builder
- Cognitive Services – Vision, speech, language and decision APIs
- Bot Service – Framework for intelligent chatbots and virtual agents
Google Cloud Platform
- AutoML – Automated simple-to-use machine learning
- Vision API – Pre-trained models for image recognition
- Natural Language API – Text analysis and entity recognition
- Speech API – Convert speech to text
The integration, ease of use and not needing to configure environments yourself makes cloud AI services appealing. Just be aware that these managed offerings can lock you into particular technology stacks.
Selecting the Right Framework as a Beginner
As you can see, there is an abundance of artificial intelligence frameworks now available. Here are some key criteria to guide your decision when starting out:
- Language: What programming languages are you skilled in already? Can help determine easiest framework.
- Functionality: Do you need a general ML library or one focused on NLP/computer vision/etc?
- Community: How strong is the user community if you need help troubleshooting?
- Industry Use: Will skills transfer if seeking AI jobs at tech companies?
- Scalability: Does it allow seamlessly scaling from prototypes up to enterprise solutions?
To simplify the decision, think first about your primary programming language. Then consider one of the major multipurpose frameworks above that aligns. TensorFlow or PyTorch in Python are hard to go wrong with thanks to their versatility. Scikit-learn also remains a popular starting point in data science.
Don‘t overlook cloud services if you value speed in getting applications running without infrastructure setup. Provider lock-in is the main downside to evaluate based on future portability needs.
Getting Started with AI Frameworks
Once you‘ve selected a framework, how do you actually begin using it? Here are some tips:
- Take Tutorials: Most frameworks offer getting started tutorials on their websites. These walk through core functionality.
- Leverage Documentation: Comprehensive docs with code examples are essential references as you use frameworks.
- Seek Sample Apps: Many frameworks supply sample applications demonstrating end-to-end development.
- Join Forums: Developers and data scientists in community forums can provide guidance applying frameworks.
- Do Online Courses: Structured online courses focusing on hands-on examples a good way to ramp up.
Be patient in the initial learning phase with an AI framework. Start simple then incrementally increase complexity as your skills build. Referencing documentation and examples will fast track getting over the beginner hurdle.
Current Trends to Watch
The machine learning framework landscape continues evolving at a rapid pace. Here some current trends to monitor that may impact future direction:
- Multimodal models: Combining different data types like text, images and speech in single models requires specialized frameworks.
- Better interoperability: Allowing interoperability between the exploding number of narrowly focused frameworks is an area of focus.
- Hybrid cloud: Using both cloud services and custom frameworks is becoming more popular for flexibility.
- Leaner frameworks: Constraints of edge devices is driving miniaturized frameworks with only essential capabilities.
- Automated machine learning (AutoML): Automating tedious framework coding tasks to increase accessibility of AI.
We only scratched the surface introducing common beginner AI frameworks here. But this should provide a solid foundation to get you up and running with artificial intelligence application development in areas like computer vision, natural language processing or general machine learning.
Once you master an initial framework, you can then expand your skills from there across the wide range of other exciting emerging tools in this space. The future is bright when it comes to being a developer or practitioner in artificial intelligence!
