Stochastic Gradient Descent Courses and Certifications

Deep Learning Essentials

Explore neural networks, perceptrons, and deep learning fundamentals. Master backpropagation, stochastic gradient descent, and kernel methods through Python coding assignments.

• Coursera
• 15 hours 41 minutes
• On-Demand
• Paid Course

Fundamentals of Natural Language Processing

13 ratings at Coursera

Explore the foundations of NLP through probabilistic language models, neural networks, and vector-based meaning representations, learning to implement N-gram models, text classifiers, and word embeddings.

• Coursera
• 1 day 6 minutes
• On-Demand
• Paid Course

Neural Networks Made Easy

64 ratings at Udemy

Comprehensive introduction to neural networks, covering fundamentals, perceptrons, and coding from scratch. Includes hands-on challenges and focuses on deep understanding without relying on libraries.

• Udemy
• 1 hour 59 minutes
• On-Demand
• Free Online Course

Applied Natural Language Processing in Engineering Part 1

Discover NLP fundamentals from text classification to neural networks, optimization techniques, word embeddings, and parsing methods for engineering applications.

• Coursera
• 1 day 3 hours 45 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

Intro to Deep Learning

4 reviews

Use TensorFlow and Keras to build and train neural networks for structured data.

• Kaggle
• 4 hours
• On-Demand
• Free Certificate

Information Theory of Deep Learning - Naftali Tishby

Explores the connection between deep learning and information theory, offering new insights into neural network design, generalization, and optimization through an information bottleneck framework.

• YouTube
• 1 hour 25 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

Clustering & Classification

Discover k-means clustering and logistic regression to segment data into meaningful groups and predict class membership probabilities with practical applications.

• Brilliant
• On-Demand
• Paid Course

Practical Deep Learning for Coders - Full Course

Comprehensive introduction to deep learning, covering theory and practical applications. Learn to build and deploy models, understand ethics, and explore various AI techniques. No advanced math required.

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

Alexander Amini;  MIT

MIT: Introduction to Deep Learning

Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.

• YouTube
• 45 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

Alfredo Canziani

NYU Deep Learning - Spring 2020

Master neural networks, CNNs, RNNs, GANs, and transformers through hands-on PyTorch implementation covering optimization, energy-based models, and applications in CV and NLP.

• YouTube
• 1 day 17 hours 12 minutes
• On-Demand
• Free Video

Alfredo Canziani

Optimisation

Explore optimization techniques for neural networks, including gradient descent, SGD, momentum, adaptive methods, and normalization layers, with real-world applications in MRI technology.

• YouTube
• 1 hour 29 minutes
• On-Demand
• Free Video

Simons Institute

The Information Bottleneck Theory of Deep Neural Networks

Explore the Information Bottleneck Theory's application to deep neural networks, examining statistical learning, information theory, and stochastic gradient descent in brain data analysis.

• YouTube
• 1 hour 38 minutes
• On-Demand
• Free Video