Machine Learning with Python: from Linear Models to Deep Learning

25 reviews

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

• edX
• 10-14 hours a week, 15 weeks long
• Self paced
• Free Online Course (Audit)

Convolutional Neural Networks for Visual Recognition (Spring 2017)

5 reviews

This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.

• YouTube
• 20 hours worth of material
• On-Demand
• Free Online Course

DeepLearning.AI;  Stanford

Neural Networks and Deep Learning

15 reviews

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will l…

• Coursera
• 25 hours worth of material, 4 weeks long
• 6th Mar, 2023
• Free Online Course (Audit)

Adversarial debiasing with partial learning - medical image studies | Ramon Correa

Syllabus: Intro Bias in ML models Background: Studying Biases in Medical AI models These Algorithms Look at X-Rays-and Somehow Detect Your Race Solution: Retrain Model With Balance Dataset Emory CXR dataset Balanced Training TPR disparities persist in "b…

• YouTube
• Less than 1 hour of material
• On-Demand
• Free Online Course

Deep Learning Methods for Electrocardiograms and Echocardiograms | Weston Hughes

Syllabus: Introduction Overview What are electrocardiograms Current AI systems What is an ECG Results Analysis Lime Explainability Echocardiograms Questions

• YouTube
• 1-2 hours worth of material
• On-Demand
• Free Online Course

Stanford Seminar: HPC Opportunities in Deep Learning - Greg Diamos, Baidu

Syllabus: Introduction. Success this year. Deep speech 2. Deep learning scales. The opportunity for HPC. Workload characteristics. Beware of ignoring work efficiency. Beware of ignoring speed of light. Dense compute. Fast Interconnects. Elastic…

• YouTube
• 1-2 hours worth of material
• On-Demand
• Free Online Course

Stanford Seminar - Deep Learning in Speech Recognition

Syllabus: Introduction. Birth of Artificial Intelligence. Checkers (Arthur Samuel, 1956). ELIZA (Weizenbaum 1966). 2001 Space Odyssey (Stanley Kubrick, 1968). Deep Blue (IBM, 1997). Deep Learning (Hinton, 2006). Jeopardy (IBM, 2011). The imitatio…

• YouTube
• 1-2 hours worth of material
• On-Demand
• Free Online Course

Stanford Seminar - Deep Learning for Medical Diagnoses

Syllabus: Introduction. Traditional Model of Clinical. Diagnostic Procedure with Al. Information Gathering Step. Diagnostic Testing with Al. Machine Learning Framework. Next Paradigm shift?. Arrhythmia detection. Future of continuous monitoring.…

• YouTube
• Less than 1 hour of material
• On-Demand
• Free Online Course

Stanford Seminar - Persistent and Unforgeable Watermarks for DeepNeural Networks

Syllabus: Introduction. DNNS ARE INCREASINGLY POPULAR. DEEP NEURAL NETWORK (DNN). DNNS ARE HARD TO TRAIN. TWO WAYS TO BUY MODELS FROM COMPANIES. IP PROTECTION FOR MODEL OWNER. WATERMARKS ARE WIDELY USED FOR OWNERSHIP PROOF. THREAT MODEL. ATTACKS…

• YouTube
• 1-2 hours worth of material
• On-Demand
• Free Online Course

Stanford CS330: Deep Multi-Task and Meta Learning

Syllabus: Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 1 - Introduction & Overview. Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 2 - Multi-Task & Meta-Learning Basics. Stanford CS330: Multi-Task and Meta-Learning, 2019 | L…

• YouTube
• 18 hours worth of material
• On-Demand
• Free Online Course

Stanford Seminar - Deep Learning for Symbolic Mathematics - Guillaume Lample & Francois Charton

Syllabus: Introduction. Deep learning for symbolic mathematics. Starting point. Basic intuition. The plan. From expressions to trees. Generating data. Symbolic integration (forward approach). Symbolic integration (backward approach). Symbolic in…

• YouTube
• Less than 1 hour of material
• On-Demand
• Free Online Course

MIT 6.S191: Introduction to Deep Learning 2021

2 reviews

Deep Learning is subset of machine learning focused on extracting patterns from data using neural networks and use those patterns to inform the learning tasks. It is all about teaching computers how to learn a task from raw data. The course will start…

• YouTube
• 29 hours worth of material
• On-Demand
• Free Online Course

MIT 16.412J Cognitive Robotics, Spring 2016

1 review

This is an advanced lecture series from MIT 16.412 Cognitive Robotics of Spring 2016, led by MIT students.

• YouTube
• 8 hours worth of material
• On-Demand
• Free Online Course

Deep Learning in Life Sciences - Spring 2021

Learn Deep Learning in Life Sciences with Prof. Manolis Kellis from MIT! In this spring 2021 course, you will learn about AI, ML, machine learning foundations, Convolutional Neural Networks CNNs, Recurrent Neural Networks RNNs, Graph Neural Networks GNNs…

• YouTube
• 31 hours worth of material
• On-Demand
• Free Online Course

EI Seminar - Yejin Choi - Intuitive Reasoning as (Un)supervised Neural Generation

Syllabus: Introduction Observations Common Gen Summary Deep Learning as a Human New Relations Language Models Descriptive Ethics Dynamic Context Questions Multimodal Information Combining Representations Question Selfsupervised learning Unique computatio…

• YouTube
• 1-2 hours worth of material
• On-Demand
• Free Online Course