Webinar: 5 Factors to Consider in Developing Deep Learning Projects

AI technology is transforming entire industries. However, implementing deep learning effectively can be a complex and daunting task, with many potential pitfalls along the way.

Join Ofri Masad, Head of AI at Deci and a former tech CTO, as he shares valuable insights on:

  • Common challenges faced by AI teams today
  • 5 factors to consider in developing deep learning projects
  • How to reduce risk, time & cost when developing DL applications

Discover key strategies and best practices that can help you to navigate the challenges of deep learning and maximize its potential for your organization.

Watch now, and if you want to learn more about building better models for your use case, book a demo here.

You May Also Like

[Webinar] How to Speed Up YOLO Models on Snapdragon: Beyond Naive Quantization

[Webinar] How to Evaluate LLMs: Benchmarks, Vibe Checks, Judges, and Beyond

[Webinar] How to Boost Accuracy & Speed in Satellite & Aerial Image Object Detection

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")