7 Best Deep Learning Frameworks You Should Know in 2024

As we are now moving at this speed in technology, we’re becoming more adaptive toward it. In recent years, there has been a lot of noise about Deep Learning, especially in the field of technology (to be specific, data science), and it’s been widely used today in various industries. The Deep Learning Market is expected to be around US$ 25.50 Billion by 2025

Deep Learning, Machine Learning has become one of the most significant weapons in technology today including self-driving cars, automated tasks, AI-based voice-overs, and whatnot, it is widely operating in almost every domain to make work-life balance simpler and more advanced. Why this has become an urge of this technology and most demanding in every corner of the world? To help you with that, we’re here with this article to discuss the anomaly of deep learning and the frameworks that are being widely used today. 

What is a Deep Learning Framework?

A deep learning framework is a software library or tool that includes a set of APIs (Application Programming Interfaces), abstractions, and tools to assist developers in building and training deep learning models. Deep learning frameworks could help you upload data and train a deep learning model that would lead to accurate and intuitive predictive analysis. These frameworks simplify the process of creating and deploying neural networks, allowing researchers and engineers to focus on complicated machine-learning tasks.

7 Best Deep Learning Frameworks You Should Know in 2024

Explore these deep-learning frameworks designed to advance your projects and boost your results. Whether you’re a beginner eager to work on your first project or an experienced developer trying to find something new in AI innovation, this list provides you with the knowledge to choose the perfect framework for your needs. Let us move on to check out the 7 best deep learning frameworks you should know in 2024.

1. TensorFlow

TensorFlow is one of the most popular, open-source libraries that is being heavily used for numerical computation deep learning. Google introduced it in 2015 for their internal RnD work but later when they saw the capabilities of this framework, they decided to make it open and the repository is available at TensorFlow Repository. As you’ll see, learning deep learning is pretty complex but making certain implementations is far easier, and by such frameworks, it’s even smoother to process the desired outcomes.

How Does it Work?

This framework allows you to create dataflow graphs and structures to specify how data travels through a graph with the help of inputs as tensors (also known as a multi-dimensional graph). Tensor Flow allows users to prepare a flowchart and based on their inputs, it generates the output.

Applications of Tensor Flow:

• Text-Based Application: Nowadays text-based apps are being heavily used in the market including language detection, sentimental analysis (for social media to block abusive posts)
• Image Recognition (I-R) Based System: Today most sectors have introduced this technology in their system for motion, facial, and photo-clustering models.
• Video Detection: Real-time object detection is a computer vision technique to detect the motion (from both image and video) to trace back any object from the provided data.

2. PyTorch

The most famous, that even powers “Tesla Auto-Pilot” is none other than Pytorch which works on deep learning technology. It was first introduced in 2016 by a group of people (Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan), under Facebook’s AI lab. The interesting part about PyTorch is that both C++ & Python can use it but Python’s interface is the most polished. Not so surprisingly, Pytorch is being backed by some of the top giants in the tech industry (Google, Salesforce, Uber, etc.). It was introduced to achieve two major goals, the first is to remove the requirement of NumPy (so that it can power GPU with tensor) and the second is to offer an automatic differentiation library (that is useful to implement neural networks).

How Does it Work?

This framework uses a computational dynamic graph right after the declaration of variables. Besides this, it uses Python’s basic concepts like loops, structures, etc. We have often used NLP functions in our smartphones (such as Apple’s Siri or Google Assistant), and they all use deep learning algorithms known as RNN or Recurrent Neural Network.

Applications of PyTorch:

• Weather Forecast: To predict and highlight the pattern of a particular set of data, Pytorch is being used (not only for forecast but also for real-time analysis).
• Text Auto Detection: We might have noticed sometimes whenever we try to search something on Google or any other search engine, it starts showing “auto-suggestion” and that’s where the algorithm works and Pytorch is being used
• Fraud Detection: To prevent any unauthorized activities on credit/debit cards, this algorithm is being used to apply anomalous behavior and outliers.

3. Keras

Since we’ve been talking about deep learning and its complexity, Keras is another highly productive library that focuses on solving deep learning problems. Besides this, Keras also helps engineers to take full advantage of the scalability and cross-platform capabilities to apply within their projects. It was first introduced in 2015 under the ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) project. Keras is an open-source platform and is being actively used as a part of Python’s interface in machine learning and deep neural learning. Today, big tech giants like Netflix, Uber, etc. are using Keras actively to improve their scalability.

How Does it Work?

The architecture of Keras has been designed in such a way that it acts as a high-level neural network (written in Python). Besides this, It works as a wrapper for low-level libraries (such as TensorFlow or Theano) and high-level neural network libraries. It was introduced with the concept of performing fast testing and experiments before going on the full scale.

Applications of Keras:

• Today, companies are using Keras to develop smartphones powered by machine learning and deep learning in their system. Apple company is one of the biggest giants that has incorporated this technology in past few years.
• In the healthcare industry, developers have built a predictive technology where the machine can predict the patient’s diagnosis and can also alert pre-heart attack issues. (Thus, this machine can predict the chances of detecting heart disease, based on provided data).
• Face Mask Detection: During the pandemic, many companies have offered various contributions, and companies have built a system using deep learning mechanisms for using facial recognition to detect whether the person is wearing a facial mask or not. (Nokia was among the companies to initiate this using the Keras library)

4. Theano

To define any mathematical expressions in deep learning, we use Python’s library Theano. It was named after a great Greek mathematician “Theano”. It was released in 2007 by MILA (Montreal Institute for Learning Algorithms) and Theano uses a host of clever code optimizations to deliver as much performance at maximum caliber from your hardware. Besides this, there are two salient features at the core of any deep-learning library:

• The tensor operations, and
• The capability to run the code on CPU or Graphical Computation Unit (GPU).

These two features enable us to work with a big bucket of data. Moreover, Theano proposes automatic differentiation which is a very useful feature and can also solve numeric optimization on a big picture than deep learning complex issues.

How Does it Work?

If you talk about its working algorithm, Theano itself is effectively dead, but the deep learning frameworks built on top of Theano, are still functioning which also include the more user-friendly frameworks- Keras, Lasagne, and Blocks that offer a high-level framework for fast prototyping and model testing in deep learning and machine learning algorithms.

Applications of Theano:

• Implementation Cycle: Theanos works in 3 different steps where it starts by defining the objects/variables then moves into different stages to define the mathematical expressions (in the form of functions) and at last it helps in evaluating expressions by passing values to it.
• Companies like IBM are using Theanos for implementing neural networks and to enhance their efficiency
• For using Theanos, make sure you have pre-installed some of the following dependencies: Python, NumPy, SciPy, and BLAS (for matrix operations).

5. Deeplearning4j (DL4J)

Deeplearning4j (DL4J) is a free tool, a deep learning framework for building applications using Java and Scala. It was created by Skymind the reason behind its popularity is that it works well with existing Java-based systems, thanks to its compatibility with the Java Virtual Machine (JVM). DL4J lets developers make and use strong models for things like recognizing images and speech, understanding language, and making predictions.

How Does it Work?

Deeplearning4j (DL4J) helps developers by providing a set of libraries for Java and Scala programmers to build and deploy deep learning models. It takes advantage of the Java Virtual Machine (JVM) for compatibility and supports various neural network architectures. DL4J makes easy tasks like image and speech recognition, natural language processing, and predictive analytics. Its emphasis on distributed computing enables efficient training of large-scale models across multiple machines.

Application of Deeplearning4j (DL4J)

• DL4J is suited for integration with existing systems due to its compatibility with the Java Virtual Machine (JVM).
• DL4J is good at training large deep-learning models because it can work on many machines at once.
• DL4J is widely used in various domains such as image and speech recognition, natural language processing, and predictive analytics, making it a versatile choice for different tasks.

6. Scikit-learn

Originating from the notion SciPy Toolkit was designed to operate and handle high-performance linear algebra. Firstly, it was introduced back in 2007 during the Google Summer of Code project by David Cournapeau. This model is designed on various frameworks such as NumPy, SciPy, and Matplotlib and has been written in Python. The objective of sci-kit-learn is to offer some of efficient tools for Deep learning, Machine learning, and statistical modeling that enlist:

• Regression (Linear and Logistic)
• Classification (K-Nearest Neighbors)
• Clustering (K-means and K-means++)
• Model Selection,
• Preprocessing (min to max normalization), and
• Dimensionality reduction (used for visualization, summarization, and feature selection)   

Moreover, it offers two different varieties of algorithms (supervised and unsupervised).

How Does it Work?

The sole purpose of introducing this library is to achieve the level of robustness and support required for use in production systems, which means a deep focus on concerns (that include ease of use, code quality, collaboration, documentation, and performance). Although the interface is Python, c-libraries are an advantage for performance (such as NumPy) for arrays and matrix operations.

Application of Scikit-learn

• Companies like Spotify, Inria, and J.P. Morgan are actively using this framework to improve linear algebra and statistical analysis.
• It works on the user’s behavior and displays the outputs based on their activity
• It helps in collecting data, analyzing those stats, and providing satisfactory outputs of what users would want to see. (just like booking flight tickets or doing online shopping)

The course on Machine Learning Basic and Advanced – Self-Paced gives you access to the course explaining ML and AI concepts such as Regression, Classification, and Clustering, and you will get to learn all about NLP. 

7.  Sonnet

Sonnet is a high-level toolkit for creating sophisticated neural network architectures in TensorFlow. This deep learning framework is built on top of TensorFlow. Sonnet seeks to construct and generate Python objects that correspond to certain parts of a neural network. These objects are then individually linked to the computational TensorFlow graph. This approach of independently building Python objects and attaching them to a graph simplifies the construction of high-level structures. This is one of the greatest deep-learning frameworks available.

How Does it Work?

Sonnet, crafted by DeepMind, is a user-friendly deep learning framework used for constructing neural networks with TensorFlow. It simplifies the creation of models through high-level abstractions, modular design, and efficient parameter management. Sonnet also works well with TensorFlow, making it easier for scientists and developers to create and train smart systems for different jobs. It’s like a friendly assistant that makes constructing and optimizing sophisticated models straightforward.

Application of Sonnet

• Sonnet plays a crucial role in advanced neural network research, offering a flexible framework for quickly trying out new ideas in model architectures and optimization methods.
• In NLP, a Sonnet is used to build language models like transformers, making it great for tasks such as understanding and generating text, like in text classification, sentiment analysis, and language generation.
• For computer vision tasks such as recognizing images and finding objects, Sonnet is valuable. It smoothly works with TensorFlow and supports GPU acceleration, making designing and training models efficient.

Conclusion

Once you understand the basics of the main deep learning frameworks, you can confidently select the one that fits your project needs. It’s crucial to identify the most suitable framework for your particular use or implementation. Each deep learning framework, like TensorFlow, PyTorch, Keras, Sonnet etc comes with its unique advantages. You are not bound to one framework in your building of deep learning models journey; feel free to switch between them based on your project requirements. This flexibility ensures you can optimize your approach and achieve the best results.