Seeking Resources for Learning Neural Nets and Variational Autoencoders
Published:Dec 23, 2025 23:32
•1 min read
•r/datascience
Analysis
This Reddit post highlights the challenges faced by a data scientist transitioning from traditional machine learning (scikit-learn) to deep learning (Keras, PyTorch, TensorFlow) for a project involving financial data and Variational Autoencoders (VAEs). The author demonstrates a conceptual understanding of neural networks but lacks practical experience with the necessary frameworks. The post underscores the steep learning curve associated with implementing deep learning models, particularly when moving beyond familiar tools. The user is seeking guidance on resources to bridge this knowledge gap and effectively apply VAEs in a semi-unsupervised setting.
Key Takeaways
- •The post highlights the difficulty of transitioning from scikit-learn to deep learning frameworks like Keras, PyTorch, and TensorFlow.
- •The user is working on a project using Variational Autoencoders (VAEs) for financial data in a semi-unsupervised manner.
- •The primary challenge is a lack of practical experience with the deep learning tools despite a conceptual understanding of the underlying principles.
Reference
“Conceptually I understand neural networks, back propagation, etc, but I have ZERO experience with Keras, PyTorch, and TensorFlow. And when I read code samples, it seems vastly different than any modeling pipeline based in scikit-learn.”