Frameworks of the Future: Decoding the Power of PyTorch and TensorFlow in Artificial Intelligence

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AI Unlocked

EVO AI

16 episodes

4 days ago

Join the experts at EVO AI as we unlock the power of Artificial Intelligence. We cover everything from Machine Learning & Artificial Neural Networks to GANs, MLOps, AIOps and Deep Learning. We look at latest developments, available platforms & technologies and how we can use these new emerging tools to get ahead. All in an easy-to-understand format that includes both technical details Q&As and "how-to" episodes allowing everyone to join in: from AI-curious folk all the way to entrepreneurs and executives. Tune in every Saturday evening if you're eager to stay at the forefront of AI innovation.

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Frameworks of the Future: Decoding the Power of PyTorch and TensorFlow in Artificial Intelligence

AI Unlocked

38 minutes 41 seconds

1 year ago

Frameworks of the Future: Decoding the Power of PyTorch and TensorFlow in Artificial Intelligence

In this "AI Unlocked" episode, we will cover the following:

Pytorch and TensorFlow Overview: Both are key AI frameworks with diverse applications in AI.
Development and Features: PyTorch, by Facebook AI Research, offers a dynamic computation graph and user-friendly interface. TensorFlow, created by Google, is known for robustness and scalability.
Core Differences: PyTorch uses dynamic graphs and is easier to learn, while TensorFlow has a static graph and includes Keras for structured development.
Implementation and Usage: Open-source, compatible with Python and GPU-accelerated. Used for model building, data preparation, training, and evaluation.
Performance Benchmarks: Performance varies across different AI models. Both support optimization techniques and distributed training.
Recent Developments: TensorFlow is better for CNNs, PyTorch excels in BERT and RNNs. GPU performance is hardware-dependent.
Use Cases and Popularity: TensorFlow is widely used in healthcare and finance, PyTorch in automotive and entertainment. Strong community support for both.
Transfer Learning and Training: Both support transfer learning, TensorFlow uses Keras API, PyTorch offers model flexibility.
Future Directions: TensorFlow focuses on distributed training and edge computing, PyTorch on user-friendliness and mobile deployment.
Conclusion: These frameworks are vital for various AI applications beyond training LLMs (large language models).