Courses from 1000+ universities
$7.2 billion in combined revenue since 2020. $8 billion in lost market value. This merger marks the end of an era in online education.
600 Free Google Certifications
Marketing
Cybersecurity
Machine Learning
Circuits and Electronics 1: Basic Circuit Analysis
Academic Writing Made Easy
Nutrition, Exercise and Sports
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Dive into advanced LLM inference deployment using AWS's ml.p4de.24xlarge instance, exploring BLOOM 176B implementation with DeepSpeed and DJL for optimal performance and scalability.
Explore the latest KerasNLP toolbox for building and customizing BERT transformer models, with hands-on guidance for both pre-trained and domain-specific implementations using TensorFlow 2.
Master BERT model pre-training, fine-tuning, and inference using TF2 and KERAS NLP through hands-on implementation of transformers for domain-specific tasks.
Explore Google's Sparrow AI system, its architecture, and comparison with ChatGPT, featuring demonstrations and insights into next-generation conversational search technology and language models.
Master BERT model pre-training from scratch using PyTorch, focusing on domain-specific data. Build custom tokenizers, design architecture, and implement masked language modeling for specialized neural information retrieval systems.
Discover effective strategies for learning and implementing Vision Transformer technology through top academic resources, code repositories, and research platforms like HuggingFace and Papers with Code.
Discover how to leverage SetFit and SBERT for zero-shot classification by creating and utilizing synthetic data, enhancing performance in both zero-shot and few-shot scenarios.
Master text vectorization techniques in Keras preprocessing layers for transforming natural language inputs into numerical sequences and building efficient AI input processing pipelines.
Master node and edge classification in GraphML using GraphSAGE, covering DGL implementation, heterogeneous graphs, and practical PyTorch examples for effective graph neural network development.
Master link prediction in graph neural networks using DGL and PyG frameworks, implementing node connectivity prediction through practical code examples and negative sampling techniques.
Master heterogeneous graph analysis using PyG and SBERT for node classification, with hands-on implementation in PyTorch Geometric and practical applications in social networks and cybersecurity.
Master Graph Convolutional Networks (GCN) implementation in PyG for node classification, featuring practical coding examples and theoretical foundations of spectral graph convolutions.
Dive into advanced feature scaling with KERAS Feature Space, building a medical AI system to predict heart attack probabilities using structured data classification and optimized data pipelines.
Explore the evolution of data warehousing from traditional ETL to modern Lakehouse architecture, focusing on Delta Lake, Structured Streaming, and PySpark implementation with practical code examples.
Discover essential qualifications and strategies for securing high-paying AI positions across consulting, biotech, and pharmaceutical industries, with insights from real job postings and market demands.
Get personalized course recommendations, track subjects and courses with reminders, and more.