Backpropagation Courses and Certifications

Neural Networks: Zero to Hero

Discover how to build neural networks from scratch, progressing from basic backpropagation to modern GPT transformers through hands-on implementation of language models.

• Independent
• 14 hours 32 minutes
• On-Demand
• Free Online Course

Alexander Amini;  MIT

MIT: Recurrent Neural Networks

2 reviews

Explore deep sequence modeling with recurrent neural networks, including LSTMs and attention mechanisms. Learn to tackle challenges like vanishing gradients and long-term dependencies in sequence tasks.

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

Andrej Karpathy

Building Makemore - Becoming a Backprop Ninja

Hands-on tutorial on manual backpropagation through a 2-layer MLP, enhancing understanding of gradient flow in neural networks for confident innovation and debugging.

• YouTube
• 1 hour 55 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

Recurrent Neural Networks and Transformers

Comprehensive exploration of recurrent neural networks, LSTMs, and attention mechanisms in deep learning, covering theory, implementation, and applications for sequence modeling tasks.

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

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

Alexander Amini;  MIT

MIT 6.S191 - Recurrent Neural Networks

Explore recurrent neural networks, LSTMs, and attention mechanisms for sequence modeling. Learn backpropagation through time, gradient issues, and practical applications in deep learning.

• YouTube
• 45 minutes
• 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

Sebastian Lague

How to Create a Neural Network and Train It to Identify Doodles

Comprehensive guide to building and training neural networks from scratch, covering key concepts and practical implementation for image recognition tasks.

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

Python Engineer

Deep Learning With PyTorch - Full Course

Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.

• YouTube
• 4 hours 36 minutes
• On-Demand
• Free Video

Alfredo Canziani

History, Motivation, and Evolution of Deep Learning

Explore the history, motivation, and evolution of deep learning, covering key concepts like gradient descent, backpropagation, CNNs, and feature extraction in neural networks.

• YouTube
• 1 hour 39 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Modern Computer Vision - Advanced Deep Learning Techniques for Image Processing

Explore advanced deep learning techniques for image processing, covering CNNs, RNNs, object detection, 3D reconstruction, and practical computer vision applications.

• YouTube
• 1 day 7 hours 34 minutes
• On-Demand
• Free Video

Data Science Courses

Supervised Learning - STAT 841 Fall 2017

Master supervised learning fundamentals including LDA, PCA, neural networks, SVMs, and CNNs through comprehensive statistical machine learning theory and applications.

• YouTube
• 17 hours 33 minutes
• On-Demand
• Free Video

Data Science Courses

Deep Learning - Fall 2015

Master deep learning fundamentals through neural networks, CNNs, RNNs, Word2vec, and practical implementation with Theano and Keras frameworks.

• YouTube
• 16 hours 46 minutes
• On-Demand
• Free Video

Data Science Courses

Machine Learning and Classification - STAT 441/841 CM 763 - Fall 2015

Master classification algorithms from linear discriminants to neural networks, covering PCA, SVM, decision trees, boosting, and PAC learning theory.

• YouTube
• 20 hours 42 minutes
• On-Demand
• Free Video