Activation Functions Courses and Certifications

AssemblyAI

Deep Learning Explained

Discover neural networks, transformers, and NLP fundamentals through comprehensive tutorials covering backpropagation, regularization, RNNs, and modern LLM techniques.

• YouTube
• 3 hours 11 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Modules and Architectures

Explores deep learning modules through softargmax, cost functions, multiplicative interactions, mixture-of-experts, and parameter transformations.

• YouTube
• 1 hour 42 minutes
• Self-Paced
• Free Video

NDC Conferences

Writing a Neural Net from Scratch

Learn to build a neural network for image recognition from scratch in C#, covering key concepts like gradient descent and backpropagation without complex math or external libraries.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Conference Talk

MIT HAN Lab

Basics of Neural Networks - EfficientML.ai Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

John Savill's Technical Training

Understanding ChatGPT: From Machine Learning to Language Models

Explore machine learning, neural networks, and GPT technology. Gain insights into the evolution and inner workings of ChatGPT, from basic concepts to advanced applications.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

Donato Capitella

Neural Networks and Multi-Layer Perceptrons - Understanding the Fundamentals

Dive into the fundamentals of neural networks, exploring artificial neurons, multi-layer perceptrons, and vectorization techniques for optimized GPU processing and efficient network implementation.

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

Alexander Amini

MIT Introduction to Deep Learning - 6.S191 - Lecture 1

Dive into the foundations of deep learning with MIT's comprehensive lecture series, exploring core concepts and principles with Alexander Amini.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

CodeEmporium

Deep Learning 101 - Zero to Hero for Deep Learning

Discover neural networks from scratch with hands-on implementation, backpropagation, optimizers, loss functions, and advanced techniques like transfer learning and NLP applications.

• YouTube
• 2 hours 29 minutes
• Self-Paced
• Free Video

Jeff Heaton

Deep Neural Network Basics for Keras and TensorFlow

Explains how feed-forward neural networks compute outputs through weighted sums, bias neurons, activation functions, and gradient descent.

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

NDC Conferences

Writing a Neural Net from Scratch

Learn to code a neural network for image recognition in C# from scratch, covering gradient descent, activation functions, and backpropagation without complex math or external libraries.

• YouTube
• 57 minutes
• Self-Paced
• Conference Talk

MIT HAN Lab

Basics of Neural Networks - EfficientML.ai Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.

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

MIT HAN Lab

EfficientML.ai - Basics of Neural Networks - Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.

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

MIT HAN Lab

Basics of Neural Networks - Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques for efficient machine learning model development.

• YouTube
• 1 hour 12 minutes
• Self-Paced
• Free Video

Introduction to Neural Networks - Part II - Lecture 8

Explores nonlinear activation functions and multilayer perceptrons for binary and multiclass image classification.

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

VinAI

Mehler's Formula, Branching Processes, and Compositional Kernels of Deep Neural Networks

Explore deep neural networks through compositional kernels and branching processes. Gain insights into activation functions, network limits, and memorization capacity. Learn about eigenvalues and a new random features algorithm.

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