Activation Functions Courses and Certifications

The MLP Architecture: Activations & Initialization in R

Master MLP architecture in R by stacking layers, implementing ReLU and Softmax activations, and applying weight initialization strategies like He and Xavier for effective neural network training.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Training Neural Networks: the Backpropagation Algorithm

Master the backpropagation algorithm by implementing loss functions, gradient descent, and SGD optimizers to train neural networks using R.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

ACM SIGPLAN

Convex Hull Approximation for Activation Functions

Discover WraAct, a novel approach for efficiently constructing tight over-approximations of activation function convex hulls to enhance neural network verification precision and speed.

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

Enigmatic Autoencoders for Dimensionality Reduction

Discover how autoencoders compress and reconstruct data through neural networks in R, mastering unsupervised learning techniques for effective dimensionality reduction.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Neural Networks Fundamentals: Neurons and Layers

Discover how neurons process information and build dense layers from scratch using JavaScript through hands-on implementation and activation functions.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

The MLP Architecture: Activations & Initialization in JavaScript

Master MLP architecture by building complete neural networks from scratch, implementing ReLU & Softmax activations, and applying proper weight initialization strategies in JavaScript.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Time Series Forecasting with LSTMs

Master LSTM networks for time series forecasting by building, optimizing, and evaluating models that handle long-term dependencies effectively.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Credential

Build a Neural Network from Scratch in R

Master neural network construction in R from neurons to backpropagation, building your own library to tackle real-world ML challenges without relying on black-box solutions.

• CodeSignal
• 10 hours
• Self-Paced
• Free Certificate
• 4 courses

Credential

Build a Neural Network from Scratch in Javascript

Master neural network fundamentals by coding from scratch in JavaScript, covering neurons, backpropagation, and real-world ML applications without relying on black boxes.

• CodeSignal
• 10 hours
• Self-Paced
• Free Certificate
• 4 courses

Neural Networks

Master neural network fundamentals by implementing perceptrons, activation functions, and backpropagation from scratch in C++ without high-level libraries.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Build, Train and Deploy ML Models with Keras on Google Cloud

2811 ratings at Coursera

Learn to design, build, and scale ML models using TensorFlow and Keras. Covers data pipelines, neural networks, and training on Vertex AI for efficient, large-scale machine learning projects.

• Coursera
• 13 hours 18 minutes
• Self-Paced
• Paid Course

Introduction to Neural Networks and PyTorch

1901 ratings at Coursera

Learn to build and train deep neural networks using PyTorch, covering tensors, linear regression, logistic regression, CNNs, and transfer learning for practical AI applications.

• Coursera
• 18 hours 4 minutes
• Self-Paced
• Paid Course

Introduction to Neural Networks and Deep Learning with Python

Explore neural networks and deep learning with Python, covering optimization, regularization, transfer learning, and autoencoders to build, train, and evaluate models for real-world AI applications.

• edX
• 32 hours
• Self-Paced
• Free Online Course (Audit)

Alexander Amini;  MIT

Introduction to Deep Learning

1 rating

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.

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

Deep Learning

34 ratings at Coursera

Explore neural networks, CNNs, RNNs, transformers, generative models, and advanced techniques in this comprehensive introduction to deep learning for aspiring ML engineers and data scientists.

• Coursera
• 2 days 9 hours 8 minutes
• Self-Paced
• Paid Course