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WeAreDevelopers LIVE • Jun 17, 2020

The pitfalls of Deep Learning - When Neural Networks are not the solution

Adrian Spataru , Bohdan Andrusyak

Deep learning isn't a silver bullet for enterprise data. Discover why tree-based models often outperform neural networks and when to avoid their massive implementation costs.

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#1 about 3 min

Contrasting classical machine learning with deep learning approaches

An overview of the differences between manual feature extraction in classical algorithms and automatic extraction in neural networks.

#2 about 4 min

Successful applications and representation learning in neural networks

How deep learning drives breakthroughs in autonomous driving, language translation, and music generation through powerful feature representations.

#3 about 3 min

Real-world failures and unrealistic expectations of deep learning

How out-of-context translations and flawed facial recognition demonstrate the severe consequences of algorithmic limitations.

#4 about 4 min

Evaluating data quantity constraints in complex neural networks

Why deep learning struggles to identify accurate patterns when provided with limited or scarce data sets.

#5 about 2 min

The critical requirement for high quality training data

How garbage or incomplete data negatively impacts model accuracy regardless of algorithmic sophistication.

#6 about 4 min

Overcoming deep learning challenges with tabular business data

Why tree-based boosting methods consistently outperform neural networks on limited historical business spreadsheets.

#7 about 4 min

Comparing model explainability between algorithms and neural networks

The importance of understanding algorithm decisions and discarding irrelevant features by leveraging simple decision trees.

#8 about 2 min

Production complexity and engineering costs of oversized models

How excessive model constraints and large file sizes prevent improved architectures from being deployed into production.

#9 about 3 min

Analyzing computational resources and cloud training costs

The astronomical financial requirements needed to train massive natural language processing models from scratch.

#10 about 2 min

Calculating the business value and return on investment

Determining if marginal accuracy improvements from deep learning justify the additional technical complexity and overhead.

#11 about 2 min

Leveraging transfer learning to reduce data dependency issues

Reusing pre-trained models on new datasets accelerates development while avoiding high data and processing requirements.

#12 about 3 min

Exploring interpretability and future advancements in tabular processing

Discovering recent research around transparent tabular transformers and assessing readiness before integrating neural architectures.

Matching moments

1:35 min

Root causes of underlying AI initiative failures

Deivids Vilkinsons Deivids Vilkinsons +3 · World Congress 2026 Europe

1:58 min

Understanding core ingredients for successful AI adoption

Data Dan Data Dan · World Congress 2025

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

4:55 min

Business motivations and adoption challenges for generative AI

Maxim Salnikov Maxim Salnikov · LIVE

1:20 min

Uncovering the hidden technical debt in machine learning

Nico Axtmann · World Congress 2022

2:56 min

The gap between data science maturity and business value

Simon Stiebellehner · World Congress 2021

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