ML Fundamentals

Error Analysis with Hamel Husain: Using W&B Tables for Model Evaluation

Hyperparameter Tuning with W&B Sweeps

What do Computer Scientists Read? - Computerphile

Geospatial Machine Learning with Kumar Chellapilla - 607

FastAPI for Machine Learning: Live coding an ML web application.

Fireside Chat #8: Navigating the Full Stack of Machine Learning

Masterclass: Servitization - Selling products through services | Indian School of Business

Rosanne Liu - career creation for non-standard candidates

Building Cohere API Demo App With Streamlit | Adrien Morisot

On the Adversarial Robustness of Deep Learning

Building drones to restore deforestation

#41 Machine Learning Specialization [Course 1, Week 3, Lesson 4]

Stanford CS229M - Lecture 14: Neural Tangent Kernel, Implicit regularization of gradient descent

How to Assemble a Deep Learning Machine - Full Process | Part 2

Engineering Production NLP Systems at T-Mobile - 600

Fireside Chat #7: How to Build an Enterprise Machine Learning Platform from Scratch

The RS-232 protocol

Sean Carroll | The Biggest Ideas in the Universe: Space, Time and Motion | Talks at Google

Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks

Role of Optimization in Machine Learning & Deep Learning | DataHour | Analytics Vidhya

Train & deploy your deep learning models with Kubeflow integrated with Amazon SageMaker

Markov Decision Processes - Computerphile

Need All Your Help To Test NeuroLabs Coding Platform Today At 6pm

An Integration Conundrum - Numberphile

Using W&B Tables and Reports for Enhanced EDA in MLOps

#40 Machine Learning Specialization [Course 1, Week 3, Lesson 4]

#39 Machine Learning Specialization [Course 1, Week 3, Lesson 4]

#38 Machine Learning Specialization [Course 1, Week 3, Lesson 4]

#37 Machine Learning Specialization [Course 1, Week 3, Lesson 4]

#36 Machine Learning Specialization [Course 1, Week 3, Lesson 3]

#30 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

#29 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

#28 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

#27 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

#26 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

#25 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

#24 Machine Learning Specialization [Course 1, Week 2, Lesson 1]

How to Build a Real-World AI Project Portfolio

Branching out of the Notebook: ML Application Development with GitHub

The 3 factors that Determine the success of ML projects

Is the premise of data-centric AI flawed?

The 4 main tasks in the production ML lifecycle

What even is a 10x ML engineer?

Why data scientists love and hate notebooks: velocity and validation

ML engineering vs traditional software engineering: similarities and differences

Easily connect your ML models to data lakes using Amazon SageMaker Studio - AWs Online Tech Talk

Smart Manufacturing Episode #3: How Machine Learning and IIoT Improve OEE - AWS Online Tech Talks

Making Sense of Health Data with Amazon HealthLake - AWS Online Tech Talks