Feedforward Neural Networks Courses and Certifications

IIT Ropar;  NPTEL

Deep Learning - IIT Ropar

Explore deep learning architectures, optimization algorithms, and applications in computer vision and NLP. Gain practical knowledge for solving real-world problems using neural networks and advanced techniques.

• Swayam
• 12 weeks
• Self-Paced
• Free Online Course

Code Your Own Llama 4 LLM from Scratch - Full Course

Master the fundamentals of Llama 4 by coding it from scratch, exploring tokenization, self-attention mechanisms, rotary positional embeddings, and feed-forward networks with practical implementations.

• freeCodeCamp
• 3 hours 29 minutes
• Self-Paced
• Free Video

LLM from Scratch Tutorial - Code and Train Qwen 3

Master building Qwen 3 from scratch with hands-on coding, architecture deep-dive, attention mechanisms, training loops, and real-time gradient visualization in this comprehensive tutorial.

• freeCodeCamp
• 1 hour 3 minutes
• Self-Paced
• Free Video

NPTEL;  IISc Bangalore

Pattern Recognition

This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier

• YouTube
• 1 day 5 hours
• Self-Paced
• Free Video

Jay Alammar

The Narrated Transformer Language Model

Visual introduction to the Transformer architecture, explaining its components and applications in AI/ML. Accessible to various experience levels, covering tokenization, embedding, and output projection.

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

Jeff Heaton

Introduction to Neural Networks for Java

Master neural network programming fundamentals in Java through hands-on implementation of algorithms, backpropagation, genetic algorithms, and real-world applications.

• YouTube
• 6 hours 48 minutes
• Self-Paced
• Free Video

Jeff Heaton

Introduction to Neural Networks Programming in C# - 2009

Discover neural network programming fundamentals in C# through hands-on implementation of backpropagation, genetic algorithms, and real-world applications like stock prediction.

• YouTube
• 6 hours 49 minutes
• Self-Paced
• Free Video

Jeff Heaton

Neural Network Calculation - Feedforward and Recurrent Network Fundamentals

Master feedforward and recurrent neural network calculations through step-by-step demonstrations of activation functions, output computation, and Elman/Jordan architectures.

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

Aleksa Gordić - The AI Epiphany

Neural Style Transfer

Dive into neural style transfer from basic theory to advanced implementations, covering optimization and feed-forward methods with hands-on PyTorch coding examples.

• YouTube
• 1 hour 33 minutes
• Self-Paced
• Free Video

MITCBMM

Connections Between Physics and Deep Learning

Explore the fascinating intersections of physics and deep learning, from neural networks to scale-invariant behavior, with insights from MIT's Max Tegmark.

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

Data Science Courses

Deep Learning - STAT 940 - Fall 2023

Master neural networks from feedforward architectures to transformers, GANs, and diffusion models in this comprehensive university-level deep learning program.

• YouTube
• 23 hours 31 minutes
• Self-Paced
• Free Video

GOTO Conferences

Composing Bach Chorales Using Deep Learning

Explore how deep learning and LSTM can compose Bach-style chorales, with insights from a musical Turing test revealing AI's potential for musical creativity.

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

MIT HAN Lab

Transformer and Large Language Models - Part I - Lecture 12

Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.

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

MIT HAN Lab

Transformer and Large Language Models - Part I - Lecture 12

Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

MITCBMM

Feedforward and Feedback Processes in Visual Recognition

Explore visual recognition processes, comparing deep learning models with neuroscience-inspired approaches. Gain insights into complex visual reasoning and potential advancements in AI through computational neuroscience.

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