Neural Networks Courses and Certifications

Programming Transformer Neural Networks with PyTorch

Build Transformer models from scratch in PyTorch, then fine-tune pre-trained models for NLP tasks such as text generation and question answering.

• Udacity
• 4 hours
• Self-Paced
• Paid Course

3Blue1Brown

Gradient Descent, How Neural Networks Learn

Using handwritten digit recognition, this visual lesson explains how neural networks learn by minimizing a cost function with gradient descent.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Andrej Karpathy

Intro to Neural Networks and Backpropagation - Building Micrograd

Builds a minimal neural-network library from scratch to reveal backpropagation, gradient descent, and training under the hood.

• YouTube
• 2 hours 26 minutes
• Self-Paced
• Free Video

Kaggle;  Amazon Web Services;  Amazon Career Certificate

Introduction to Machine Learning with TensorFlow

5 ratings

Build supervised models with linear regression, decision trees, Naive Bayes and SVMs, apply K-means and Gaussian mixtures, and train a TensorFlow neural network image classifier.

• Udacity
• 49 hours
• Self-Paced
• Paid Course

Neural Networks for Machine Learning

26 ratings

Learn how artificial neural networks work and how to train them well, with basic algorithms and practical tricks applied to speech and object recognition, image segmentation, and language modeling.

• Coursera
• 16 weeks, 7-9 hours a week
• No Longer Offered
• Paid Course

Tech with Tim

Python Pong AI Tutorial - Using NEAT

Implement NEAT to train a Python Pong AI through neural-network evolution, then test the trained agent.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Parameter Sharing - Recurrent and Convolutional Nets

Explores parameter sharing in recurrent and convolutional neural networks, including LSTMs, attention, backpropagation, and convolutional architectures.

• YouTube
• 2 hours
• Self-Paced
• Free Video

Alfredo Canziani

Neural Nets - Rotation and Squashing

Explore how affine transformations and nonlinear activations combine into neural networks, with intuitive visualizations and hands-on 2×2 implementations in PyTorch.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Conf42

Superposition in LLM Feature Representations

Explore mechanistic interpretability and superposition in LLM feature representations, focusing on neural network qualities, linearity, and sparsity to uncover hidden features and improve model understanding.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Artem Kirsanov

Predictive Coding: A Biologically Plausible Alternative to Backpropagation

Explore Predictive Coding, a biologically plausible alternative to backpropagation for neural networks, derived from first principles and explained through energy formalism, update rules, and neural connectivity.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Graham Neubig

CMU Advanced Natural Language Processing 2022

Master advanced NLP techniques from text classification to transformers, covering attention mechanisms, pre-training methods, prompting, bias detection, and structured learning algorithms.

• YouTube
• 1 day 6 hours 23 minutes
• Self-Paced
• Free Video

Graham Neubig

CMU Advanced Natural Language Processing 2021

Master advanced NLP techniques from neural networks to transformers, covering text classification, language modeling, attention mechanisms, and cutting-edge pre-training methods.

• YouTube
• 1 day 6 hours 17 minutes
• Self-Paced
• Free Video

Learn to create, train, and test neural networks in PyTorch while using loss functions and optimizers to predict real-world data.

• Codecademy
• 3 hours
• Self-Paced
• Free Trial Available

Computational Neuroscience

9 ratings

Model neural computation with spiking neuron and Hodgkin-Huxley models, decode brain activity, and study Hebbian, supervised, and reinforcement learning in networks using Matlab/Octave/Python.

• Coursera
• 8 weeks, 3 hours a week
• Self-Paced
• Paid Course

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

2 ratings

Learn TensorFlow 2.0 with Python through machine learning fundamentals, neural networks, CNN-based vision, RNN-based NLP, and Q-learning.

• freeCodeCamp
• 7 hours
• Self-Paced
• Free Video