Loss Functions Courses and Certifications

PyTorch: Fundamentals

75 ratings at Coursera

Master PyTorch fundamentals through hands-on coding, building neural networks from single neurons to CNNs, managing datasets, and implementing complete ML workflows with confidence.

• Coursera
• 1 day 10 minutes
• On-Demand
• Paid Course

Packt

Foundations and Core Concepts of PyTorch

50 ratings at Coursera

Master PyTorch fundamentals: from setup to advanced modeling. Build neural networks, optimize performance, and apply deep learning techniques to real-world problems using hands-on exercises and practical implementations.

• Coursera
• 6 hours 49 minutes
• On-Demand
• Paid Course

Packt

Deep Learning - Artificial Neural Networks with TensorFlow

Explore deep learning and artificial neural networks using TensorFlow. Learn foundational concepts, implement ANN models, and optimize deep learning models through hands-on examples and practical applications.

• Coursera
• 7 hours 21 minutes
• On-Demand
• Paid Course

Packt

Foundations of Deep Learning and Neural Networks

Master deep learning fundamentals, from neural network basics to advanced CNNs. Explore backpropagation, regularization techniques, and practical applications using TensorFlow and Keras. Ideal for tech professionals and students.

• Coursera
• 14 hours 19 minutes
• On-Demand
• Paid Course

Coursera

Model Training & Evaluation

Master machine learning model training and evaluation through PyTorch practice, exploring mini-batch training, learning-rate schedulers, loss curve analysis, and class-imbalance techniques.

• Coursera
• 2 hours 35 minutes
• On-Demand
• Paid Course

Institute of Human Anatomy

Fat - The Science Behind Body Fat Types, Functions, and Weight Loss

Discover the science behind body fat, from its various types and essential functions to effective fat loss strategies and debunking common myths about fat burning zones and targeted reduction.

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

Neural Networks from Scratch in Python

Build and understand neural networks from the ground up, covering key concepts like neurons, layers, activation functions, and loss calculation in Python.

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

CodeEmporium

Deep Learning 201 - Mathematical Foundations

Dive into neural networks' mathematical foundations covering activation functions, optimizers, CNNs, RNNs, LSTMs, GANs, and VAEs with practical explanations.

• YouTube
• 2 hours 19 minutes
• On-Demand
• Free Video

Stanford Seminar - Deep Learning in Speech Recognition

Explore deep learning's evolution and impact on speech recognition, from early AI to modern applications like Siri, covering key concepts, historical milestones, and practical implementations.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Online Course

Learn to create, train, and test artificial neural networks using PyTorch. Build predictive models, define functions, and evaluate performance for real-world applications.

• Codecademy
• 3 hours
• On-Demand
• Free Trial Available

PyTorch and Monai for AI Healthcare Imaging - Python Machine Learning Course

2 reviews

Develop AI algorithms for liver segmentation using PyTorch and Monai. Learn computer vision techniques, data preparation, and model training for healthcare imaging applications.

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

Deep Learning Crash Course for Beginners

2 reviews

Comprehensive introduction to deep learning fundamentals, covering neural networks, learning types, architectures, and model development steps. No coding required, ideal for beginners.

• freeCodeCamp
• 1-2 hours
• On-Demand
• Free Video

TensorFlow: Neural Networks and Working with Tables

142 ratings at LinkedIn Learning

Learn about neural networks and working with tabular data using TensorFlow.

• LinkedIn Learning
• 43 minutes
• On-Demand
• Free Trial Available

Kavli Institute for Theoretical Physics

Representation Learning and Custom Loss Functions for Atmospheric Data - Christian Lessig

Explores representation learning and custom loss functions for atmospheric data analysis, advancing climate science through machine learning techniques to extract insights from complex Earth system observations.

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

Alan Turing Institute

CoPhy-PGNN: Learning Physics-Guided Neural Networks with Competing Loss Functions

Explore physics-guided neural networks with competing loss functions for solving eigenvalue problems, focusing on innovative approaches in scientific computing.

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