ML Fundamentals

GridSearchCV- Select the best hyperparameter for any Classification Model

January 30, 2019 - MCIT Online Admissions Webinar with Chris Murphy

Precision, Recall and F1-Score

Prepare su conjunto de datos para el aprendizaje automático - Episodio 2

Spinning Black Holes

Deep learning for fundamental sciences using high-performance computing (O’Reilly AI Conference)

a16z Podcast | Holy Non Sequiturs, Batman: What Disruption Theory Is ... and Isn't

Advanced Flutter Project - Third BLoC, StreamControllers and Periodic Observables - Part Six

Separable Filters and a Bauble - Computerphile

Advanced Flutter Project - Filtering Contributions and Building Aesthetics - Part Five

What's your Favourite Programming Language? (sound check Q) - Computerphile

Deep Learning Specialization by deeplearning.ai — Trailer

But why is a sphere's surface area four times its shadow?

Five Firsts for Mars InSight

Understanding choice and life in Neural networks

Understanding evolution with neural networks using Pixling

How to pick a machine learning model 5: Navigating assumptions

How to pick a machine learning model 1: Choosing between models

Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!

AI Ethics, Strategic Decisioning and Game Theory with Osonde Osoba - TWiML Talk #192

¿Qué es una Red Neuronal? Parte 3.5 : Las Matemáticas de Backpropagation | DotCSV

Advanced Flutter Project - Adding Fonts and Fuzzy Timestamps - Part Four

Flutter Best Practices - Building a Generic BLoC Provider - Advanced Flutter Project Part Three

TensorFlow high-level APIs: Part 2 - going deep on data and features

Advanced Flutter Project - Adding a Second BloC - Part Two

TensorFlow high-level APIs: Part 1 - loading data

Machine Learning with Tensorflow on Google Cloud Platform Specialization — Trailer

Data Science Specialization by Johns Hopkins University - Trailer

BEAST & The GPU Cluster - Computerphile

Basic Modeling for Discrete Optimization - Module 1 Summary by The University of Melbourne #8

Basic Modeling for Discrete Optimization - Global Constraints by The University of Melbourne #7

Basic Modeling for Discrete Optimization - Arrays and Comprehensions by University of Melbourne #6

Basic Modeling for Discrete Optimization - Modeling Objects by The University of Melbourne #5

Basic Modeling for Discrete Optimization - Models and Instances by The University of Melbourne #4

Basic Modeling for Discrete Optimization - Third Model by The University of Melbourne #3

Basic Modeling for Discrete Optimization - Second Model by The University of Melbourne #2

Basic Modeling for Discrete Optimization - First Steps by The University of Melbourne #1

Basic Modeling for Discrete Optimization - Welcome to Basic Modeling by The University of Melbourne

TensorFlow AutoGraph (TensorFlow @ O’Reilly AI Conference, San Francisco '18)

IBM Blockchain Foundation for Developers - Why blockchain is relevant for business by IBM #3

IBM Blockchain Foundation for Developers - The Problem Area by IBM #2

IBM Blockchain Foundation for Developers - The business backdrop by IBM #1

How to pick a machine learning model 2: Separating signal from noise

How to pick a machine learning model 3: Choosing a loss function

How to pick a machine learning model 4: Splitting the data

Infinite Data Structures: To Infinity & Beyond! - Computerphile

ASU Master of Computer Science Live Admissions Q&A

How optimization for machine learning works, part 4