Courses from 1000+ universities
Two years after its first major layoff round, Coursera announces another, impacting 10% of its workforce.
600 Free Google Certifications
Graphic Design
Data Analysis
Digital Marketing
El rol de la digitalización en la transición energética
First Step Korean
Supporting Successful Learning in Primary School
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Philosopher McKenna explores the interplay of art, psychedelics, culture, and consciousness, examining the artist's role in society and psychedelics' impact on creativity and perception.
Learn to set up Automatic1111 UI and Stable Diffusion for AI art creation. Hands-on approach with project-based learning to explore image generation using artificial intelligence.
Learn to manage environments and process images for deep Q-networks in reinforcement learning, focusing on the cart and pole problem implementation.
Learn to build, plot, and interpret a confusion matrix using PyTorch for neural network evaluation, including techniques for disabling gradient tracking and analyzing model performance across different classes.
Learn to build a convolutional neural network training loop using Python and PyTorch. Gain practical skills in implementing deep learning algorithms for image processing tasks.
Explore PyTorch's inner workings, including OOP concepts, convolutional and linear layer weight tensors, and matrix multiplication for deep learning in neural networks.
Learn to build a convolutional neural network using PyTorch for computer vision and AI applications. Gain hands-on experience in implementing object-oriented neural network architectures.
Learn to implement batch normalization in convolutional neural networks using PyTorch. Explore the benefits, create and compare networks, and optimize performance through hands-on coding and practical examples.
Learn to build neural networks using PyTorch's Sequential class. Explore multiple ways to construct models, set random seeds for reproducibility, and understand the benefits of this approach for creating efficient deep learning architectures.
Debugging session exploring PyTorch DataLoader's inner workings, focusing on data normalization, batch building, and parameter impacts. Insights into efficient data handling for deep learning projects.
Learn to normalize datasets in PyTorch using torchvision.transforms.Normalize(). Explore feature scaling, standardization, and color channel normalization. Understand the impact on neural network training.
Get personalized course recommendations, track subjects and courses with reminders, and more.