Alexander Amini Courses

MIT: Recurrent Neural Networks

2 ratings

Explore deep sequence modeling with recurrent neural networks, including LSTMs and attention mechanisms. Learn to tackle challenges like vanishing gradients and long-term dependencies in sequence tasks.

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

Neurosymbolic AI

2 ratings

Explore neurosymbolic AI, combining deep learning and symbolic reasoning to address limitations in current AI systems. Learn about out-of-distribution performance, adversarial examples, and advantages of hybrid approaches.

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

Deep Generative Modeling

1 rating

Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI techniques.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

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

Visualization for Machine Learning - Google Brain

1 rating

Explore data visualization techniques for machine learning, including high-dimensional data, multilingual systems, and language models, with insights on biases and user intent.

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

Sequence Modeling with Neural Networks

1 rating

Explore sequence modeling with neural networks, focusing on RNNs, their challenges, and solutions like gated cells. Learn applications in music generation and machine translation.

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

MIT Introduction to Deep Learning

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

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

Trustworthy Deep Learning

Explore robust and trustworthy deep learning, covering algorithmic bias, uncertainty estimation, and innovative approaches to create risk-aware AI systems for more reliable and ethical machine learning applications.

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

Reinforcement Learning

Explore deep reinforcement learning concepts, from Q-functions to policy gradients, with applications in gaming, robotics, and AI. Gain insights into cutting-edge techniques and real-world implementations.

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

Recurrent Neural Networks, Transformers, and Attention

Explore recurrent neural networks, transformers, and attention mechanisms in deep learning. Learn design principles, applications, and advanced concepts like LSTM and backpropagation through time.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Deep Generative Modeling

Explore deep generative modeling, including autoencoders, VAEs, GANs, and diffusion models. Learn about latent variables, training techniques, and recent advances in generative AI.

• YouTube
• 1 hour
• Self-Paced
• Free Video

Convolutional Neural Networks

Explore convolutional neural networks for computer vision, covering feature extraction, convolution operations, and applications like object detection and self-driving cars.

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

Reinforcement Learning - MIT 6.S191 Lecture 5

Comprehensive exploration of deep reinforcement learning, covering Q-functions, policy gradients, and real-world applications like AlphaGo. Ideal for understanding RL fundamentals and advanced concepts.

• YouTube
• 1 hour
• Self-Paced
• Free Video

MIT 6.S191 - Automatic Speech Recognition

Explore automatic speech recognition with Rev.com experts, covering data selection, speech input processing, and advanced ASR techniques like attention-based models and language modeling.

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

MIT: Reinforcement Learning

Explore deep reinforcement learning concepts, algorithms, and real-world applications in this comprehensive lecture from MIT's Introduction to Deep Learning course.

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