The Best Deep Learning Courses in 2026

Deep learning is the branch of machine learning behind image recognition, speech models and the large language models in tools like ChatGPT. It trains neural networks with many layers to find patterns in data that simpler models miss.

The hard part is choosing a starting point. Some courses are mostly math, some are mostly code, and many still teach libraries and techniques that the field has moved past.

This list covers the best deep learning courses for beginners, for theory, for computer vision, for language models, for Hugging Face, and for training large models.

Course ratings, review counts and learner numbers last verified 12 October 2026.

Best Deep Learning Courses Compared

Best Deep Learning Courses For Beginners

The best deep learning course for beginners gets you training a working neural network quickly, then explains what happened. You need basic Python for all 3 picks except our no-code primer.

1. Introduction to Deep Learning with PyTorch

Our most-taken deep learning course, and the first course in our Deep Learning in Python track.

• Level: Intermediate
• Time: 4 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Python users who want to build and train their first neural network

The course covers network architecture, hyperparameters, the training loop, and evaluating and improving models for classification and regression problems. It has 92K learners and takes about 4 hours.

It was refreshed in Jan 2026, so the PyTorch code matches current versions. Our catalogue lists it at intermediate level because it assumes you can already write Python.

2. Understanding Machine Learning

Our no-code introduction to machine learning, which deep learning builds on.

• Level: Beginner
• Time: 2 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Complete beginners who want to know what a model is before writing one

This course explains supervised and unsupervised learning, model training and where deep learning fits, without any code. With 312K learners, it is the most-taken course in this article.

It is not a deep learning course on its own. Take it first if terms like training data and overfitting are new to you, then move straight to Introduction to Deep Learning with PyTorch.

3. Practical Deep Learning for Coders (fast.ai)

A free course by Jeremy Howard that teaches deep learning top-down, starting with working models.

• Level: Intermediate. You need to be comfortable in Python.
• Time: Self-paced, across a series of long video lessons
• Cost: Free
• Best for: Programmers who learn best by building first and reading theory later

The course has you training an image classifier in the first lesson, then works through computer vision, text, tabular data and recommendation systems. It uses the fastai library, which sits on top of PyTorch.

It is the best free deep learning course available. The trade-off is that fastai hides some of the PyTorch detail that most job descriptions ask for.

Our pick: Start with our Introduction to Deep Learning with PyTorch if you want PyTorch skills you can put on a CV. Choose fast.ai's Practical Deep Learning for Coders if cost matters more than structure.

Best Deep Learning Course For Theory And Foundations

A foundations course explains why neural networks learn: backpropagation, optimisation, regularisation and initialisation. These picks suit learners who want to debug a model that will not train, not only build one that does.

1. Deep Learning Specialization (DeepLearning.AI, on Coursera)

Andrew Ng's 5-course program, with nearly 1 million learners per its Coursera listing.

• Level: Intermediate. Some linear algebra and calculus helps.
• Time: About 3 months at 10 hours a week, per its Coursera listing
• Cost: Coursera subscription
• Best for: Learners who want the theory explained carefully from first principles

The specialization covers neural network basics, tuning and regularisation, structuring ML projects, convolutional networks and sequence models. Andrew Ng explains the maths step by step, which is why it is still the standard recommendation for theory.

Its programming assignments use TensorFlow, while most current research code uses PyTorch. Expect to learn PyTorch separately if you need it for work.

2. Intermediate Deep Learning with PyTorch

Our follow-on course, which covers the training problems that stop real models working.

• Level: Intermediate
• Time: 4 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Learners who can build a basic network and now need it to train reliably

The course covers training networks that converge reliably, convolutional networks for images, recurrent networks such as LSTMs and GRUs for sequences, and multi-input and multi-output architectures. It takes about 4 hours.

It holds a 4.8/5 rating from 2,387 reviews. It is more practical than the specialization above and much shorter.

Our pick: Take DeepLearning.AI's Deep Learning Specialization if you want the theory in full. If you need working PyTorch skills sooner, our Intermediate Deep Learning with PyTorch covers the foundations that matter most in 4 hours.

Best Deep Learning Courses For Computer Vision

Computer vision is the use of deep learning to classify, detect and segment what is in an image. These courses suit learners who have finished an introductory deep learning course.

1. Deep Learning for Images with PyTorch

Our main computer vision course, and the third course in our Deep Learning in Python track.

• Level: Advanced
• Time: 4 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Learners who want to build image classifiers, object detectors and segmentation models

The course covers convolutional neural networks, object detection, image segmentation and image generation in PyTorch. It takes about 4 hours.

Our catalogue lists it at advanced level. Take Intermediate Deep Learning with PyTorch first if you have not used PyTorch beyond the basics.

2. Image Modeling with Keras

Our convolutional network course for learners who work in Keras.

• Level: Advanced
• Time: 4 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Teams with an existing Keras or TensorFlow codebase

We are not quoting a star rating for this course. It has 94 reviews, below the 200 we require before we publish a score.

With 40K learners and a content refresh in Jan 2026, it is still a solid choice if Keras is your framework. Choose the PyTorch course above if you are starting fresh.

Our pick: Take our Deep Learning for Images with PyTorch. Choose Image Modeling with Keras only if your team already works in Keras.

Best Deep Learning Courses For Text And Transformers

Transformers are the neural network architecture behind modern language models, including GPT and Claude. These courses take you from classic text models to building and using transformers yourself.

1. Transformer Models with PyTorch

Our course on building a transformer from its components.

• Level: Advanced
• Time: 2 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Learners who want to understand how LLMs work inside, not only call one

The course starts with the building blocks of transformer models, then has you assemble them into full transformer architectures in PyTorch. At 2 hours, it is one of the shortest courses in this article.

It was refreshed in Jul 2026, the most recent update among our deep learning courses here, and holds a 4.8/5 rating from 977 reviews.

2. Deep Learning for Text with PyTorch

Our course on text classification and generation with neural networks.

• Level: Advanced
• Time: 4 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Learners who want the full path from text preprocessing to sequence models

The course covers preparing text for deep learning, text classification, text generation, and more advanced text modelling topics in PyTorch. It takes about 4 hours.

Take it before Transformer Models with PyTorch if you are new to working with text data.

3. Introduction to LLMs in Python

Our course on using and fine-tuning pretrained language models.

• Level: Intermediate
• Time: 3 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Practitioners who want results from existing models rather than training from scratch

The course covers getting started with pretrained LLMs, fine-tuning them, and evaluating their performance. It has 37K learners.

It sits between deep learning and applied AI work. Most teams use pretrained models far more often than they train new ones.

Our pick: Take our Transformer Models with PyTorch to understand the architecture, then Introduction to LLMs in Python to put pretrained models to work.

Best Deep Learning Courses For Training Large Models

Training a large model means spreading the work across multiple GPUs and making each training step cheaper. These courses suit practitioners whose models have outgrown one machine.

1. Efficient AI Model Training with PyTorch

Our course on distributed and memory-efficient training.

• Level: Advanced
• Time: 4 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Engineers who need to train or fine-tune models too large for a single GPU

We are not quoting a star rating for this course. It has 109 reviews, below our 200 threshold, though it was refreshed in May 2026.

The course covers distributed training with Accelerator and Trainer, ways to cut training time, and memory-efficient optimisers, with a focus on large language models. It takes about 4 hours and our catalogue lists it at advanced level.

2. Scalable AI Models with PyTorch Lightning

Our course on organising PyTorch training code with Lightning.

• Level: Intermediate
• Time: 3 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Teams who want cleaner, reproducible training code

We are not quoting a star rating for this course either, with 126 reviews. It covers building modular models in PyTorch Lightning, advanced training techniques, and optimising models so they scale.

It takes about 3 hours.

Our pick: Take our Efficient AI Model Training with PyTorch if training cost or GPU memory is your problem. Choose Scalable AI Models with PyTorch Lightning if messy training code is.

Best Deep Learning Track

A track is a set of courses taken in order. It suits learners who want a structured path from first network to advanced models without choosing course by course.

1. Deep Learning in Python

Our main deep learning track, built from 5 of the PyTorch courses above.

• Level: Intermediate to advanced
• Time: About 18 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Learners who want one complete path through deep learning in PyTorch

The track runs Introduction to Deep Learning with PyTorch, Intermediate Deep Learning with PyTorch, Deep Learning for Images with PyTorch, Deep Learning for Text with PyTorch and Transformer Models with PyTorch in order. All 5 have been updated in the last 18 months.

The rating is a review-weighted average across those 5 courses, since the track page has no rating of its own.

2. Hugging Face Fundamentals

Our applied track for using pretrained deep learning models.

• Level: Beginner to intermediate
• Time: About 12 hours
• Cost: Included in a DataCamp subscription. The first chapter is free.
• Best for: Practitioners who want to use deep learning models rather than build them

The track covers Working with Hugging Face, Introduction to LLMs in Python, Multi-Modal Models with Hugging Face and AI Agents with Hugging Face smolagents. It is the faster route to shipping something that uses deep learning.

We left out our Keras Fundamentals track. 3 of its 4 courses have not had a content update in the last 18 months.

Our pick: Take our Deep Learning in Python track if you want to understand and build neural networks. Choose Hugging Face Fundamentals if you want to apply them.

How We Chose These Courses

Deep learning moves fast, and a course that taught the right tools three years ago might not be helpful. So the first thing we looked for was recent content. Every course here has been updated in the last 18 months, which is why some well-known older courses are not on the list.

We also favored those courses where you write and train real models, not just watch someone else do it. Many of our picks use PyTorch because it is what you are most likely to meet in current research papers, open-source projects, and job descriptions.

Final Thoughts

Most people should learn deep learning in PyTorch, starting with our Introduction to Deep Learning with PyTorch. Add DeepLearning.AI's specialization if you want the theory in depth, or fast.ai's course as a good option.

After the basics, follow your use case. Choose Deep Learning for Images with PyTorch for vision, Transformer Models with PyTorch for language models, or Working with Hugging Face if you want to use existing models.