THE DEEP LEARNING FRAMEWORKS RIOT 2025

1. THE DEEP LEARNING FRAMEWORKS RIOT 2025 © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi .org

2. Witnessing the developments in open-source Deep learning platforms has been a fascinating trail of developments; while powering your understanding in the way it drives the organizational systems. As an integral yet powerful subset of machine learning, deep learning has demonstrated its nuanced efficiency in leveraging large datasets to produce highly accurate models. “Prediction is the essence of intelligence” Yann LeCun, French American Computer Scientist & Godfather of Deep Learning Witnessing the developments in open-source Deep learning platforms has been a fascinating trail of developments; while powering your understanding in the way it drives the organizational systems. As an integral yet powerful subset of machine learning, deep learning has demonstrated its nuanced efficiency in leveraging large datasets to produce highly accurate models. Deep Learning Global Market Report 2025 $144.64 Billion Market Size (in USD Billion) $144.64 Billion $144.64 Billion 2024 2025 2026 2027 2028 2029 Source: The Business Research Company The presentation above highlights the growing popularity of deep learning platforms worldwide, with its market size gaining a bigger size with every passing year; growing at a staggering 2029 (The Business Research Company). 43.30% through © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

3. deep learning framework Selecting the most suited growth manifold. The world is gaga over multiple open-source platforms and data visualization tools; offering diverse capabilities to strengthen business bonds. The focus of this elaborate read would include the top 3 trending framework contenders- PyTorch, TensorFlow, and Keras. facilitates and even amplifies organizational Let us dive right in. Deep Learning and Its Criticality in 2025 Having learned so much about Deep Learning off late, it has shown a diverse range of applications and strengths that are only becoming robust with every single day. From autonomous self-driving cars to medical diagnosis, and personalized experiences taking over the scenes, Deep Learning is the way to go. Let us understand what makes it a quintessential part of today’s organic organizational streamlining and propagates strength ahead. Driving AI advancements Deep Learning has become the spine strength of many AI applications, fostering tasks that required human intervention previously, and deep learning algorithms are constantly evolving; leading to sophisticated AI applications. Industrial Revolution 4.0 Deep learning is the common pivot for all enhancements in industries including gaming, automobile, finance, and healthcare. It also powers cybersecurity, traffic management, AIdriven chatbots, AI assistants, and resource optimization. Novel Opportunities With so much left to explore, Deep Learning has just begun flexing and promises astounding career opportunities, individualized experiences, innovative applications, and increased productivity via task automation and seamless data analysis. © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

4. Popular Deep Learning Platforms for 2025 The deep learning landscape is constantly evolving; basis current trends and expert analysis, here are the top deep learning platforms that are expected to remain relevant in 2025: 1. TensorFlow: Deep Learning has become the spine strength of many AI applications, fostering tasks that required human intervention previously, and deep learning algorithms are constantly evolving; leading to sophisticated AI applications. Market Share of TenserFlow Current Customer(s) 14,247 Market Share (Est.) 38.38% Ranking #1 Source: 6sense.com Current Global Popularity: Around the world in 2025, over using TensorFlow as a data science-machine learning tool (6sense.com). 14,247 companies have started 2. PyTorch: PyTorch has gained immense popularity, specifically in the research community, due to its flexibility and ease of use. It is an open-source machine learning framework based on the Torch library; primarily developed by Meta AI. Written in Python, C++, and CUDA; it offers dynamic computation graphs and strong GPU support that makes it ideal for rapid prototyping and experimentation. Market Share of Pytorch Current Customer(s) 8,925 Market Share (Est.) 24.05% Ranking #2 Source: 6sense.com 8925 Current Global Popularity: Over data science-machine learning tool (6sense.com). companies have started using PyTorch as a powerful © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

5. 3. Keras: Keras is a high-level user-friendly API perfect for building and training neural networks; that simplifies deep learning model development. Designed to facilitate rapid experimentation and model development; it makes it a powerful tool for both beginners and experienced practitioners; that can run on top of various backends such as TensorFlow, JAX, and PyTorch. Market Share of Keras Current Customer(s) 6574 Market Share (Est.) 17.71% Ranking #4 Source: 6sense.com Current Global Popularity: Over preferred Data science and machine learning tool (6sense.com). 6574 companies have started using Keras as their Deep Dive To Platforms Quick Descript Early Ruins Francois Chollet, a French software engineer and artificial intelligence researcher formerly Senior Staff Engineer at Google. Chollet is the creator of the Keras Deep Learning Library, released in 2015. PROS Simplicity Backend Support Pre-trained Models Fast Experimentation Features and Potentialities Keras is used for creating deep models that can be productized on smartphones. It is also used for distributed training of deep learning models. Its capabilities include user-friendly design, modular and extensible, high-level abstractions, cross-platform compatibility, model subclassing, and hyperparameter tuning. Simplified deep learning, accelerated development, versatile applications, improved collaboration, and enhanced productivity are some of the powerful potentialities that Keras has to offer. CONS Improvable features No dynamic chart creation Inefficient Errors Low-level API Source : TechVidvan © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

6. Quick Descript PROS Early Ruins TensorFlow was developed by the Google Brain team for Google’s internal use in research and production. The initial version was released under the Apache License 2.0 in 2015. Google released an updated version, TensorFlow 2.0, in September 2019. Rich ML Algorithms Data visualisation Keras friendly Scalable Features and Potentialities TensorFlow can be used to develop models for various tasks, including natural language processing, image recognition, handwriting recognition, and different computation-based simulations such as partial differential equations. CONS Inconsistent Architectural limitation Dependency Symbolic loops Source : TechVidvan Quick Descript PROS Rich set of powerful APIs Easy to Debug Supports Cloud Platforms Early Ruins PyTorch’s original authors were its founder Adam Paszke, Soumith Chintala, Sam Foss, and Gregory Chanan. Began in 2016 as an internship project; Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on the Lua programming language. Flexible, faster, and provides optimizations CONS Features and Potentialities Not widely known Released in 2016, has fewer users No monitoring and visualization tools PyTorch can develop intuitive and adaptable model design, that uses dynamic computation graphs, comes with GPU acceleration (CUDA) support), Pythonic interface, modular flexible design extensive libraries, automatic differentiator (AutoGrad), supports data parallelism, API optimization, and facilitates easy debugging. Source : TechVidvan Has a smaller developer community © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

7. Investigating the Differentiator Parameters High High and low Low API level Complex, less readable Simple and concise Not easy to use Architecture Large with high performance Large with high performance Small Datasets Good debugging potential Not needed Difficult to conduct Debugging Fast, Fast, Slow, low performance high performance high performance Speed Second-most popular Third-most popular Top-most popular Popularity C++, Python Lua CUDA, Python Language © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

8. Popular Use Cases- Find Open-Source Deep Learning Platforms in Action Driving AI advancements Uses TensorFlow, enhances accuracy and diagnosis speed Predictive maintenance Uses XGBoost; prevents downtime and saves cost Sentiment analysis Uses NLP and PyTorch, improves products and services Financial fraud detection Uses SciKitLearn, reduces financial losses Autonomous vehicles Uses PyTorch to improve safety and efficiency How to Find the Right Deep Learning Platform? Choosing the most-suited deep learning platform expects you to consider your project’s specific requisites, including ease of usage, scalability, community support, and available resources. Comprehending the data size and complexity. Accuracy, performance expected, resources, and frameworks available are all important considerations to making the right choice. This exhaustive read has aimed to deduce the most legible and comprehensible take on the diversely popular deep learning frameworks, read shall enable you to decide the best choice for your future projects and yield accurate success. For a greater and more nuanced understanding of the industry, it is inevitable to empower yourself with the right certifications and global credentials that can leverage greater career boosts and futuristic skillsets. Understanding what lies ahead in the deep learning industry is like owning a magic wand to the future! their capabilities, and shortfalls. Considering this © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

9. You May Also Like: Storytelling with Data: Transforming Raw Information into Narrative Symphonies Data Science: Unlocking Careers for the Future Data Science Skills Vs. Tools: What Matters the most for Data Scientists Discover More Discover More Discover More Future Of Data Science: 10 Predictions You Should Know Data Science vs. Decision Science Master Data-Driven Decision-Making in 2024 Discover More Discover More Discover More Factsheet: Data Science Career 2025 Top 5 Must-know Data Science Frameworks Discover More Discover More © Copyright 2025. United States Data Science Institute. All Rights Reserved us dsi. org

10. DOWNLOAD TO UNDERSTAND 3 POPULAR DEEP LEARNING FRAMEWORKS WITH REGISTER NOW LOCATIONS Arizona Connecticut Illinois 1345 E. Chandler BLVD., Suite 111-D Phoenix, AZ 85048, info.az@usdsi.org Connecticut 680 E Main Street #699 Stamford, CT 06901 , info.ct@usdsi.org 1 East Erie St, Suite 525 Chicago, IL 60611 info.il@usdsi.org Singapore United Kingdom No 7 Temasek Boulevard#12-07 Suntec Tower One, Singapore, 038987 Singapore, info.sg@usdsi.org 29 Whitmore Road, Whitnash Learmington Spa, Warwickshire, United Kingdom CV312JQ info.uk@usdsi.org info@ | www. usdsi.org usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved.