Deep Learning (DL) functions in a manner similar to the human brain, taking in information and processing it sequentially. Unlike the brain, however, DL can learn from unstructured and unlabeled data without the assistance of a human instructor. To achieve this, it utilises a network of interconnected neuron nodes, which mirror the structure of a human brain.
Deep learning has opened up a world of possibilities for applications that involve decision-making, object detection, voice recognition, and language translation. This is due to its hierarchical structure, which allows computers to process data in a non-linear fashion, as opposed to traditional machine learning algorithms that process data in a linear manner. Through deep learning, computers can interpret data more accurately, with greater speed and efficiency.
It is common for individuals to use the terms “deep learning,” “machine learning,” and “artificial intelligence” interchangeably. However, it is important to be aware that deep learning is a subset of machine learning, which belongs to the larger category of artificial intelligence. Therefore, it is essential to have a strong understanding of these topics before engaging in conversations about Keras, TensorFlow, or PyTorch or making any comparisons among them.
The meaning of “Deep Learning”
Despite not being able to match the complexity of the human brain, Deep Learning is a type of Artificial Neural Network that attempts to emulate the behaviour of the human brain by studying large datasets. It consists of three distinct layers that allow it to learn from the data it is exposed to, helping it to create complex models that can be used to perform various tasks.
The use of multiple hidden layers within a neural network can help to increase the accuracy of its outputs. Deep learning is a powerful tool for artificial intelligence (AI) applications and services, allowing for the automation of analytical and physical tasks without the need for human intervention. This technique has become commonplace in many different areas, from digital assistants and voice-enabled TV remote controls to the detection of credit card fraud and even the development of self-driving vehicles.
A Deep Learning framework is defined as.
By offering a consistent interface with a high level of abstraction, deep learning (DL) frameworks make it simpler to build, train, and evaluate deep neural networks. Examples of these frameworks include Keras, TensorFlow, PyTorch, and Caffe.
No matter what your objective is – be it integrating deep learning into business operations, developing a practical product, or sharpening your skills – the first step is to select the most appropriate deep learning framework to learn. This decision is dependent on a range of considerations, and the ideal framework for any given individual is ultimately based on their own preferences and abilities. Consequently, there is no one-size-fits-all solution.
Questions that may help you choose the best DL framework are provided below.
- What characteristics do you seek for in a DL framework as a developer?
- Is it better to develop your own template from scratch or adapt an existing model?
- How can you strike a balance between productivity and control using low-level application programming interfaces (APIs)?
- Which language do you think is the best choice?
Keras: what is it?
Keras is an open-source, high-level neural network application programming interface (API) written in Python. It is built upon frameworks such as CNTK, TensorFlow, and Theano, and is designed to enable fast experimentation with deep neural networks. Keras emphasises its code’s flexibility and modifiability, and does not perform lower-level calculations itself, instead delegating them to the backend library.
In mid-2017, TensorFlow began integrating the Keras library, providing access to it through the tf.Keras module. Despite this, Keras is still able to operate independently as its own standalone library.
To illustrate how to create a basic convolutional network in Keras, consider the following code.
- Superb, advanced application programming interface.
- Everything from TensorFlow to Aesara/Theano to CNTK works together in harmony.
- In addition to being simple to understand, it’s also simple to design new structures using.
- There are a variety of ready-to-use models already in existence.
- Ideal for little data sets.
- It is possible that this framework, which is often referred to as a “frontend,” is slower than TensorFlow and other backend frameworks.
Explain Tensorflow to me.
In 2015, Google launched TensorFlow, an open source artificial intelligence platform they designed from the beginning. It has become a favourite choice due to its various levels of abstractions, compatibility with various platforms, including Android, its detailed documentation, and the support it provides for training.
For those looking to construct and implement machine learning applications, TensorFlow offers an encouraging and rapidly-growing deep learning platform, along with an extensive selection of community resources, libraries, and tools.
Neural networks can be effectively utilised as dataflow programs with the aid of symbolic arithmetic tools provided by this set. In addition, models of varying complexity can be generated and optimised through its use.
Keras, which has recently been adopted by TensorFlow, has made it difficult to make direct comparisons between the two frameworks. While it is not required for Keras users to utilise TensorFlow, we will still compare the two frameworks in order to provide a comprehensive overview.
- Computational graphs are well-supported, and so are their visualisations (via TensorBoard).
- Google’s Keras Library management support includes regular updates and new releases.
- Extremely scalable and parallel pipelines.
- Available in large quantities, TPUs are convenient.
- Possessing the ability to debug code by using a debugging approach.
- Since these APIs are so basic, there is a significant learning curve.
- Google does provide help with library management, although the documentation for new versions is often out of date.
- It’s possible the code is a bit hard to follow.
- TPUs are solely suitable for model execution and not training.
- Acceleration on other GPU brands is not supported. Similarly, in Windows, it only allows GPU programming in Python.
Explanation of the term “PyTorch.”
PyTorch is a modern deep learning framework developed by the artificial intelligence research team at Facebook in 2017 and released as an open source software on GitHub. It is renowned for its user-friendliness, adaptability, low memory requirements and dynamic computational graphs, making it a popular choice among developers. Additionally, it has a familiar feel which facilitates code readability and efficiency. These advantages have made PyTorch stand out from other similar tools in the market.
- Easy to pick up and use.
- Faster, more accurate execution is made possible by dynamic graph logic.
- Since it was built from the ground up using Python, it has a very “pythonic” feel to it.
- The CPU and the GPU may both be used.
- The ability to conduct training remotely is available.
- In production, you will need an API server.
- Visdom does not provide a comprehensive visualisation of the training process.
- While TensorFlow has widespread recognition, PyTorch is still in its infancy.
An overview of the distinctions among Keras, TensorFlow, and PyTorch
Within the domain of deep learning, it can be difficult to decide which framework is the most suitable for a particular project or user. Three of the most popular frameworks employed by both experienced data scientists and novice users alike are Keras, TensorFlow, and PyTorch. Each project has its own requirements and every developer has their own areas of expertise and preferred approaches, making the selection process difficult.