Deep Learning courses

Stanford University

Statistical Learning with R

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.

DeepLearning.AI

Browser-based Models with TensorFlow.js

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this first course, you’ll train and run machine learning models in any…

DeepLearning.AI

Calculus for Machine Learning and Data Science

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you lear…

Great Learning

Introduction to Deep Learning

This free Deep Learning course gives you a clear and structured introduction to Deep Learning concepts from the ground up. You will learn what Deep Learning is, where it fits within Artificial Intelligence and Machine Learning, and how it is used across real world applications. The course explains…

Harvard University

Applications of TinyML

Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.

Microsoft

Extract data with Azure Document Intelligence

Azure Document Intelligence uses OCR and deep learning to extract text, key-value pairs, tables, and structured data from documents. Learn how to use prebuilt models, train custom models, and build document processing solutions.

IBM

Reinforcement Learning and Deep Learning Essentials

Covers Artificial Intelligence, Data Science, Deep Learning.

DeepLearning.AI

Apply Generative Adversarial Networks (GANs)

In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adap…

DeepLearning.AI

Advanced Deployment Scenarios with TensorFlow

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you’ll explore four different scenarios you’ll encou…

MIT OpenCourseWare

Robotic Manipulation

Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), pla…

DeepLearning.AI

Custom and Distributed Training with TensorFlow

In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients. • Build your own custom training loops using GradientTape…

IBM

Accelerating Deep Learning with GPUs

Stanford University

Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022

While deep learning has achieved remarkable success in many problems such as image classification, natural language processing, and speech recognition, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multi…

IBM

PyTorch: Tensor, Dataset and Data Augmentation

Covers Artificial Intelligence, Python, PyTorch.

Stanford University

Statistical Learning with Python

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.

DeepLearning.AI

Generative Deep Learning with TensorFlow

In this course, you will: a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image. b) Build simple AutoEncoders on the familiar MNIST dataset, an…

Harvard University

Introduction to Neural Networks and Deep Learning with Python

Build practical deep learning skills for Python-savvy professionals. Learn how neural networks are structured, trained, and evaluated—and how choices like architecture, regularization, and learning rate affect performance. Explore transfer and self-supervised learning (autoencoders). Build models f…

MIT OpenCourseWare

Machine Learning for Inverse Graphics

This course covers fundamental and advanced techniques in this field at the intersection of computer vision, computer graphics, and geometric deep learning. It will lay the foundations of how cameras see the world, how we can represent 3D scenes for artificial intelligence, how we can learn to reco…

IBM

AI for Everyone: Master the Basics

Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.

DeepLearning.AI

AI and Public Health

In this course, you will be introduced to the basics of artificial intelligence and machine learning and how they are applied in real-world scenarios in the AI for Good space. You will also be introduced to a framework for problem solving where AI is part of the solution. The course concludes with…