Exploring Deep Learning Frameworks: Tools for Building Intelligent Systems

Deep learning frameworks allow you to design and utilize artificial intelligence (AI) neural networks on existing models. This enables you to skip the important upstream programming step of creating a framework from scratch, so you can begin building your AI model more quickly. 

Understanding deep learning frameworks

Deep learning frameworks are software packages that act as blueprints for designing, training, and putting to use AI neural networks. Many are free, open-source, and reasonably simple. You can think of deep learning frameworks as third-party interfaces you build your own deep learning software upon—similar to what WordPress is for blogs, for instance. 

Before you can utilize a deep learning framework, however, it’s important to know more about deep learning itself. 

In machine learning, a neural network is a program model that allows AI to learn and make decisions in a human-like way. Neural networks, not coincidentally, exist in the human brain, which is where the idea came from. Machine learning programmers developed the current neural network learning model to get AI to mimic human thought by adapting learning models to something resembling the physical infrastructure of human thought. 

A neural network consists of nodes or neurons, each with: 

• An input layer, which takes in data

• At least one hidden layer that processes data and adapts behaviors to new data

• An output layer that sends out data that the program then acts upon

Deep learning occurs when a developer programs more than three layers of neural networks into an AI model. This type of AI program is capable of highly sophisticated operations. You’ve likely heard of such deep learning applications: They’re what power self-driving cars, and they make up the sophisticated image analysis technology that can detect the presence of cancer cells that a radiologist might otherwise miss. 

To put it simply, deep learning allows your AI model to make sense of large amounts of unstructured data far more efficiently. Deep learning frameworks make things even simpler by providing you with pre-existing blueprints on which to construct your neural network. 

Types of deep learning frameworks

Numerous deep learning frameworks are available, each with its unique features, capabilities, and strengths. Some of the most popular deep learning frameworks include TensorFlow, PyTorch, Keras, Caffe, and Deeplearning4j. 

TensorFlow

TensorFlow, developed by the Google Brain Team, is a widely used, free, open-source deep learning framework. It offers a flexible and scalable platform for building various types of neural networks, including: 

• Convolutional neural networks (CNNs), which allow AI to recognize and classify visual objects 

• Recurrent neural networks (RNNs), which allow for sequential prediction in machine learning 

• Deep belief networks (DBNs), which allow AI to understand data patterns

TensorFlow's ecosystem features high-level application programming interfaces (APIs) such as Keras, as well as tools for distributed training and deployment. These allow you to program machine learning into mobile apps with relative ease. 

Adding to its flexibility profile, TensorFlow is adaptable to a variety of programming languages. It also works with many mobile devices, including Android and iOS. 

PyTorch

The PyTorch deep learning framework is written in Python, one of the most common programming languages. This makes it easier for programmers—likely already familiar with Python—to learn than many other deep learning frameworks. Facebook's AI Research group created PyTorch, and the framework is simple and usable enough that it’s become quite popular. In fact, Tesla’s Autopilot and Uber’s Pyro software use it. 

Researchers and developers favor PyTorch for its flexibility and ease of use in prototyping and experimentation. It’s also conveniently usable on Android and iOS. 

Graphs are visualizations of the deep learning process, and PyTorch's dynamic nature allows for dynamic graph construction. This means that its graphs adapt to new information—that is, they aren’t static, as with other deep learning frameworks. 

Keras

The newest edition of Keras, Keras 3, is a Pythonic deep learning framework that can run on PyTorch and TensorFlow, as well as JAX. Its scalability has made it popular with companies such as NASA, Waymo, and YouTube. 

Keras features broader usability than either TensorFlow or PyTorch alone. Keras developers’ interest in improving ease of use extends to the establishment of what they call the progressive disclosure of complexity. This means that Keras is easy for beginners to get going with and requires only incremental learning to develop more complex deep learning modalities. 

Caffe

Caffe is a deep learning framework developed by Yangqing Jia of the Berkeley AI Research (BAIR) Lab and maintained by the Berkeley Vision and Learning Center (BVLC). Many prefer it for image-based deep learning. 

Caffe features include: 

• Speed: Caffe’s deep learning software can process more than 60 million images per day. As a benchmark, consider that it takes about 1,000 images to train deep learning software to recognize a single image reliably. 

• Efficiency: Its libraries were originally written in C++, and it can handle huge data sets and make sense of them quickly. 

• Scalability: Caffe works on graphics processing units (GPUs) and central processing units (CPUs)—hardware components that allow for increased learning capability. 

Deeplearning4j

Deeplearning4j is an open-source deep learning framework written in Java that also supports Python and C++. 

Deeplearning4j is highly scalable, and its website features a good amount of community support. It also: 

• Supports GPUs 

• Is platform-neutral

• Is portable

• Is good for deep learning requiring large data sets

Adding to the framework’s flexibility, cloud services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure support Deeplearning4j. Furthermore, you can integrate it with libraries such as Apache Spark, opening up additional potential for scalability and multi-machine learning.

Explore deep learning frameworks with Coursera

If you’re interested in learning more about deep learning frameworks, look to Coursera. DeepLearning.AI offers courses such as Deep Learning Specialization and Neural Networks and Deep Learning that can help you establish a strong foundation of deep learning knowledge.

Once you’ve mastered the fundamentals of deep learning, develop your own AI model and try it out on a few different types of deep learning frameworks. This may open up new career prospects. With major tech, aeronautics, and banking firms across the world using AI, the technology is here to stay—making deep learning knowledge an in-demand skill in the coming years. Build your skills and explore deep learning on Coursera today.