Loss Functions Courses and Certifications

Centre for Quantum Technologies

Learning Quantum States with Tunable Loss Functions

Explore quantum learning frameworks through tilted empirical risk minimization, establishing sample complexity bounds and generalization guarantees for quantum state learning processes.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Deep Learning with Python: Optimizing Deep Learning Models

25 ratings at LinkedIn Learning

Leverage techniques for optimizing deep learning models and implementing them using Python.

• LinkedIn Learning
• 2 hours 1 minute
• On-Demand
• Free Trial Available

Deep Learning Interview Prep Course

Comprehensive deep learning interview preparation covering 50 common questions, from basic concepts to advanced topics like optimization, regularization, and model architecture.

• freeCodeCamp
• 4 hours
• On-Demand
• Free Video

Build a Simple Neural Network & Learn Backpropagation

Discover how to build neural networks from scratch in Python and master backpropagation fundamentals without libraries for ML engineering and data science careers.

• Zero To Mastery
• 5 hours
• On-Demand
• Paid Course

Alexander Amini;  MIT

MIT Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Aladdin Persson

YOLOv1 from Scratch

Comprehensive tutorial on implementing YOLOv1 from scratch, covering architecture, loss function, dataset preparation, training, and evaluation for object detection.

• YouTube
• 1 hour 43 minutes
• On-Demand
• Free Video

Tech with Tim

Python Neural Networks - Tensorflow 2.0 Tutorial - What Is a Neural Network?

Explore neural networks using TensorFlow 2.0, covering key concepts, architecture, and practical implementation in Python for beginners and intermediate learners.

• YouTube
• 27 minutes
• On-Demand
• Free Video

StatQuest with Josh Starmer

Gradient Descent, Step-by-Step

Comprehensive step-by-step explanation of Gradient Descent, covering its application in Machine Learning, optimization techniques, and variations like Stochastic Gradient Descent.

• YouTube
• 24 minutes
• On-Demand
• Free Video

NPTEL;  IIT Bombay

Practical Machine Learning with Tensorflow

Learn essential machine learning concepts and apply them using TensorFlow, covering key topics from basic principles to deep learning and data visualization.

• YouTube
• 4 hours 30 minutes
• On-Demand
• Free Video

Gradient Boost

Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Yannic Kilcher

Is the Problem in the Data? Examples on Fairness, Diversity, and Bias

Exploring AI bias through interactive examples on fairness, diversity, and hidden biases. Examines whether bias issues stem from data or other factors in machine learning models.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Serrano.Academy

Denoising and Variational Autoencoders

Explore autoencoders, powerful generative models for dimensionality reduction and data generation. Learn about denoising and variational autoencoders, their applications, and training techniques.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Aladdin Persson

YOLOv3 From Scratch

Comprehensive tutorial on implementing YOLOv3 object detection algorithm from scratch using PyTorch, covering architecture, dataset loading, loss function, and training setup.

• YouTube
• 1 hour 52 minutes
• On-Demand
• Free Video

Weights & Biases

Probability - Math for Machine Learning

Explore core probability concepts for machine learning, including surprises, loss functions, and Gaussians. Gain insights into the mathematical foundations of ML algorithms.

• YouTube
• 45 minutes
• On-Demand
• Free Video