Multilayer Perceptrons 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

Career Certificate

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

Introduction to Neural Networks and PyTorch

1905 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

Career Certificate

Foundations of Neural Networks

24 ratings at Coursera

Master advanced neural network concepts, from foundational theory to practical implementation in Python, while exploring ethical considerations in AI system development.

• Coursera
• 12 weeks, 4 hours/week
• Self-Paced
• Paid Course
• 3 courses

Introduction to Neural Networks

10 ratings at Coursera

Master foundational concepts of neural networks, from basic mathematics to advanced architectures like CNNs. Build practical skills in deep learning, optimization techniques, and model training through hands-on experience.

• Coursera
• 18 hours 20 minutes
• Self-Paced
• Paid Course

A deep understanding of deep learning (with Python intro)

6254 ratings at Udemy

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

• Udemy
• 2 days 9 hours 19 minutes
• Self-Paced
• Paid Course

Deep Learning for Computer Vision

25 ratings at Coursera

Master CNNs, autoencoders, GANs, ResNet, and U-Net architectures to build powerful computer vision models for image classification, generation, and segmentation tasks.

• Coursera
• 14 hours 5 minutes
• Self-Paced
• Paid Course

Deep Learning for Natural Language Processing

Master neural networks for NLP, from feedforward and recurrent networks to transformers, transfer learning, and large language models, with practical implementation skills.

• Coursera
• 21 hours 3 minutes
• Self-Paced
• Paid Course

Deep learning in Electronic Health Records - CDSS 2

Explore deep learning principles and architectures for electronic health records, focusing on time-series classification, imputation techniques, and encoding strategies using MIMIC-III database for clinical predictions.

• Coursera
• 1 day 7 hours 38 minutes
• Self-Paced
• Paid Course

CertNexus

Build Decision Trees, SVMs, and Artificial Neural Networks

14 ratings at Coursera

Learn to build decision trees, SVMs, and neural networks for machine learning and deep learning. Gain practical skills in constructing models to solve regression, classification, computer vision, and natural language processing problems.

• Coursera
• 21 hours 54 minutes
• Self-Paced
• Paid Course

Machine Learning with Neural Networks

Explore neural network fundamentals, backpropagation, regularization, and Bayesian methods for machine learning with hands-on TensorFlow implementation.

• Coursera
• 1 day 12 hours 52 minutes
• Self-Paced
• Paid Course

Applied Natural Language Processing in Engineering Part 2

Master advanced NLP techniques including RNNs, transformers, sentiment analysis, and neural machine translation through hands-on implementation with PyTorch and Hugging Face frameworks.

• Coursera
• 1 day 9 hours 37 minutes
• Self-Paced
• Paid Course

Packt

Foundations of Deep Learning and Neural Networks

Master deep learning fundamentals, from neural network basics to advanced CNNs. Explore backpropagation, regularization techniques, and practical applications using TensorFlow and Keras. Ideal for tech professionals and students.

• Coursera
• 14 hours 19 minutes
• Self-Paced
• Paid Course

Coursera

Deep Learning and Modern AI Architectures

Master deep learning by building CNNs, applying transfer learning, and comparing RNNs, transformers, and GANs for vision, sequence, and generative tasks in real-world AI roles.

• Coursera
• 4 weeks, 7 hours a week
• Self-Paced
• Paid Course