Attention Mechanisms Courses and Certifications

Advanced Computer Vision and Deep Learning

1 review

Learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application.

• Udacity
• 9 hours
• On-Demand
• Paid Course

Deconstructing the Transformer Architecture

Master the Transformer architecture by building Multi-Head Attention, feed-forward networks, positional encodings, and complete encoder/decoder layers as reusable PyTorch modules from scratch.

• CodeSignal
• 4 hours
• On-Demand
• Free Certificate

Bringing Transformers to Life: Training & Inference

Master Transformer architecture by building complete models, implementing training pipelines with synthetic datasets, and exploring autoregressive generation with beam search strategies.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Microcredential

Exploring the Transformer Architecture

Master Transformer architecture by building attention mechanisms from scratch, then fine-tune state-of-the-art NLP models using Hugging Face for real-world deployment.

• CodeSignal
• 14 hours
• On-Demand
• Free Certificate
• 4 courses

Time Series Forecasting with GRUs

Master GRU-based time series forecasting with TensorFlow, covering bidirectional models, attention mechanisms, and hybrid CNN-GRU architectures for multivariate data prediction.

• CodeSignal
• 1 hour
• On-Demand
• Free Certificate

Time Series Forecasting with GRUs

Master GRU-based time series forecasting in PyTorch with bidirectional networks, attention mechanisms, and hybrid CNN-GRU models for multivariate data prediction and evaluation.

• CodeSignal
• 1 hour
• On-Demand
• Free Certificate

Microcredential

RNNs for Time Series with PyTorch

Master time series forecasting using PyTorch by building RNNs, LSTMs, and GRUs for univariate and multivariate data, progressing to advanced hybrid models and attention mechanisms.

• CodeSignal
• 4 hours
• On-Demand
• Free Certificate
• 4 courses

Data Science: Transformers for Natural Language Processing

3025 ratings at Udemy

ChatGPT, GPT-4, BERT, Deep Learning, Machine Learning & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch

• Udemy
• 19 hours 6 minutes
• On-Demand
• Paid Course

Modern Natural Language Processing in Python

1819 ratings at Udemy

Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab

• Udemy
• 5 hours 48 minutes
• On-Demand
• Paid Course

Math Behind LLMs, Transformers and Modern Computer Vision

1074 ratings at Udemy

From Multi Head Attention and Embeddings to Transformers, Vision Transformers, Modern Image Segmentation + LLMs and SAM

• Udemy
• 6 hours 57 minutes
• On-Demand
• Paid Course

A deep understanding of AI large language model mechanisms

790 ratings at Udemy

Build and train LLM NLP transformers and attention mechanisms (PyTorch). Explore with mechanistic interpretability tools

• Udemy
• 3 days 19 hours 3 minutes
• On-Demand
• Paid Course

Explainable Machine Learning (XAI)

27 ratings at Coursera

Develop transparent and trustworthy AI solutions using explainable machine learning techniques. Implement local and global explainability methods, visualize neural networks, and explore emerging approaches for large language models.

• Coursera
• 14 hours 48 minutes
• On-Demand
• Paid Course

Stanford CS224N - Natural Language Processing with Deep Learning 2023

Master cutting-edge neural networks for NLP, from word vectors to Transformers, with hands-on tutorials covering RNNs, attention mechanisms, and modern language models.

• YouTube
• 1 day 5 hours 12 minutes
• On-Demand
• Free Video

Stanford CS224N - Natural Language Processing with Deep Learning - Winter 2021

Master neural networks, transformers, and attention mechanisms for text processing, translation, and language generation through Stanford's comprehensive deep learning approach.

• YouTube
• 1 day 7 hours 31 minutes
• On-Demand
• Free Video

Foundations of Generative AI

5 ratings at edX

Discover how generative AI creates text, images, and code through neural networks, transformers, and training methods while exploring responsible AI integration and real-world applications.

• edX
• 3 weeks, 2-3 hours a week
• On-Demand
• Free Online Course (Audit)