WeAreDevelopers LIVE • Oct 6, 2021

Getting Started with Machine Learning

Alexandra Waldherr

Want to build your first machine learning pipeline? Watch a predictive Scikit-Learn model trained live, taking you from basic statistical functions to complex deep learning architectures.

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#1 about 2 min

Modeling brain cells to understand artificial intelligence capabilities

Early artificial intelligence research focused on modeling biological brain cells to understand logic and computation.

#2 about 2 min

Evolution of machine learning algorithms and computing hardware

Breakthroughs like backpropagation and the shift from CPUs to graphical processing units accelerated machine learning capabilities.

#3 about 2 min

Distinguishing artificial intelligence from deep learning and statistics

Deep learning configures brain-like structures while machine learning leverages statistics to find patterns in data.

#4 about 6 min

Training a regression model to predict automotive emissions

Preparing a dataset with Pandas to train a random forest regressor builds predictive capabilities for emissions.

#5 about 2 min

Inspecting decision trees and identifying feature importance variables

Analyzing the underlying questions within a random forest tree reveals the most impactful predictive data features.

#6 about 2 min

Supervised, unsupervised, and reinforcement learning paradigms explained

Training methodologies range from labeled input supervision to environmental feedback loops and computational weight optimization.

#7 about 2 min

Structural advantages of deep neural networks and residual layers

Deep neural networks leverage activation functions and residual layers to process nonlinear real-world complexities effectively.

#8 about 2 min

Mitigating overfitting and underfitting in model training data

Balancing model complexity with dataset size prevents learning anomalies that cause inaccurate real-world predictions.

#9 about 5 min

Building image classification networks using noisy diagnostic datasets

Evaluating imperfect graphical data through convolutional frameworks hones classification limits and tuning rates.

#10 about 2 min

Architecture of convolutional and recurrent neural network structures

Convolutional layers isolate image shapes while recurrency and transformers track sequential context in language processing.

#11 about 3 min

Industrial applications of machine learning in autonomous vehicles

Deep learning algorithms process radar and visual data to classify objects for autonomous automotive decisions.

#12 about 3 min

Advancing biocatalyst research through quantum machine learning algorithms

Machine learning optimization of quantum circuits unlocks protein folding discoveries inside biological neural firing processes.

#13 about 18 min

Navigating data privacy boundaries and adversarial model reliability

Exploring edge cases and privacy regulations shapes the secure rollout of trustworthy predictive artificial intelligence tools.

Matching moments

2:52 min

Introduction to safety-critical machine learning in automotive contexts

Jan Zawadzki · WWC 2022

7:31 min

Addressing participant questions on liability and machine learning

Georg Kühberger +1 · LIVE

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

4:55 min

Audience Q&A on autonomous driving models and data

Liang Yu · WWC 2022

3:45 min

Familiarizing with machine learning and neural network basics

Tillman Radmer +2 · WWC 2021

2:17 min

Distinguishing between AI, machine learning, and deep learning

Mary Grygleski

Mary Grygleski · LIVE

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