Deep Learning (DL) operates akin to the human brain, systematically ingesting and processing information. DL, however, has the ability to learn from unstructured and unlabelled data without human intervention. Herein, it utilises an interconnected network of neuron nodes that emulate the structure of the human brain.
Deep learning has unlocked a plethora of possibilities for various applications such as decision-making, object detection, voice recognition, and language translation. This is due to its hierarchical configuration, which enables computers to process data non-linearly, distinct from conventional machine learning algorithms that process data linearly. By means of deep learning, computers can interpret data with greater precision, speed, and efficiency.
Individuals often intermix the use of “deep learning,” “machine learning,” and “artificial intelligence.” Nonetheless, it’s crucial to understand that deep learning is a branch of machine learning, which belongs to the wider field of artificial intelligence. Accordingly, it is fundamental to possess a firm comprehension of these subjects before discussing Keras, TensorFlow, or PyTorch or making any comparisons between them.
The Definition of “Deep Learning”
Deep Learning, despite its inability to match the complexity of the human brain, is a form of Artificial Neural Network that strives to imitate the actions of the human brain by analysing vast amounts of data as discussed in this article. Comprising three distinct layers, it acquires its knowledge from the information it is presented with, facilitating the construction of sophisticated models to execute various functions.
Employing multiple hidden layers in a neural network can enhance the accuracy of its outputs. Deep learning is a potent technology used for artificial intelligence (AI) applications and services, simplifying the automation of analytical and physical operations with no need for human intervention. This approach has become prevalent across numerous fields, ranging from digital assistants and voice-activated TV remotes to credit card fraud detection and even the creation of self-driving automobiles.
A Definition of a Deep Learning Framework
Deep learning (DL) frameworks provide a consistent interface with a high degree of abstraction, rendering it easier to construct, train and assess deep neural networks. Keras, TensorFlow, PyTorch and Caffe are amongst the most popular frameworks of this kind.
Regardless of your goal, whether it is to incorporate deep learning into business practices, build a functional product or bolster your expertise, the initial step is to choose the most appropriate deep learning framework for learning. This choice is determined by several factors, and the optimal framework for each individual will be founded on their own aptitudes and preferences. Hence, there is no single solution that fits all situations.
Outlined below are questions that could aid in the decision-making process for selecting the best DL framework for you.
- As a developer, what attributes are you looking for in a DL framework?
- Would you prefer developing your template from scratch or adapting an existing model?
- What methods can you use to achieve a balance between productivity and control when working with low-level application programming interfaces (APIs)?
- What language do you believe is the most suitable option?
What is Keras?
Written in Python, Keras is a high-level neural network application programming interface (API) that is open source. It is developed upon frameworks such as CNTK, TensorFlow and Theano, and aims to achieve speedy experimentation with deep neural networks. Keras prioritises the flexibility and adaptability of its code, and doesn’t handle lower-level calculations itself, rather assigning them to the backend library.
Starting in mid-2017, the tf.Keras module grants access to the Keras library via TensorFlow integration. Despite this, Keras can continue to function independently as its distinct library.
To construct a fundamental convolutional network in Keras, refer to the following code sample.
Merits
- An exceptional and advanced application programming interface.
- Seamless compatibility with everything from TensorFlow to Theano/Aesara to CNTK.
- Not only is it easy to comprehend, but it is also easy to create new structures with.
- There is an abundant supply of pre-existing models that are ready for use.
Demerits
- Perfect for small data sets.
- As a “frontend,” this framework may be slower than TensorFlow and other backend frameworks.
Can you describe TensorFlow?
Launched in 2015, TensorFlow is a Google-created open source platform for artificial intelligence. It has gained popularity due to its diverse abstractions, compatibility with various platforms (including Android), detailed documentation, and support for training.
If you want to develop and put machine learning applications into operation, TensorFlow provides a promising and quickly expanding deep learning platform, as well as a broad array of community resources, libraries, and tools.
This framework’s symbolic arithmetic tools allow neural networks to be efficiently implemented as dataflow programs. Moreover, models of various complexities can be produced and optimized through its utilization.
Due to Keras’ recent adoption by TensorFlow, conducting direct comparisons between the two frameworks has become challenging. Although TensorFlow is not necessary for Keras users, we will still compare the two frameworks to provide a thorough examination.
Merits
- The framework provides robust support for computational graphs, as well as their visualizations (via TensorBoard).
- Regular updates and new versions are included in Google’s Keras Library management support.
- Exceptionally scalable and parallel pipelines.
- TPUs are readily accessible in substantial amounts, making them convenient.
- It has a debugging approach that allows for code debugging.
Demerits
- The API’s inherent complexity results in a steep learning curve.
- Google does offer assistance with library management, though the documentation for new releases is frequently outdated.
- The code may be somewhat difficult to comprehend.
- TPUs are exclusively appropriate for executing models, and not for training them.
- Alternate GPU brands do not support acceleration. Additionally, GPU programming is only possible in Python on Windows.
Definition of the term “PyTorch.”
PyTorch is a contemporary framework for deep learning developed by Facebook’s artificial intelligence research team in 2017. It was released as an open source software on GitHub and is well-known for its adaptability, easy-to-use interface, low memory requirements and dynamic computational graphs, all of which make it a popular choice among developers. Moreover, it has a familiar feel that enhances code readability and efficiency, making PyTorch stand out from other similar tools available in the market.
Merits
- Simple to grasp and employ.
- Dynamic graph logic allows for faster and more precise execution.
- As it was constructed from scratch with Python, it possesses a distinctly “pythonic” vibe.
- Both the CPU and GPU can be utilized.
- Remote training capability is provided.
Demerits
- An API server is required for production.
- Visdom doesn’t offer a comprehensive view of the training process through visualizations.
- Although TensorFlow is extensively recognized, PyTorch is still in its early stages.
A summary of the differences between Keras, TensorFlow, and PyTorch
Choosing the right framework for a deep learning project can be challenging, given the many options available. Keras, TensorFlow, and PyTorch are three of the most widely used frameworks, catering to the needs of data scientists as well as novice users. Since each project has distinct requirements and every developer has individual areas of expertise and preferred methodologies, making a choice can be a difficult task.
