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Deep Learning Courses and Certifications

Where to learn deep learning in depth and which credentials to take, from neural network basics and transformers to generative models, frameworks and deployment.

73 topics•9 categories•Updated November 2026

How to Choose a Path

6 entries

Deep learning rewards building things. Learn the maths and machine learning basics, take a code-first or theory-first deep learning course, implement models in PyTorch, then specialise in vision, language or generative models and finish with deployment and a cloud or NVIDIA credential. Pair every course with projects.

01Deep LearningDeep learning frameworks and datasets hub on this site. 02Deep Learning AlgorithmsTwelve neural architectures compared. 03Deep Learning FrameworksPyTorch, TensorFlow, JAX and Keras. 04Machine Learning Courses and CertificationsThe wider machine learning learning path. 05AI Courses and CertificationsAI learning guide. 06Courses and CertificationsHub for all courses and certifications.

Step 1: Maths and Machine Learning Foundations

8 entries

Linear algebra, probability, statistics and basic machine learning come first.

07Khan Academy: linear algebraFree lessons on linear algebra. 083Blue1Brown: neural networksVisual video explanations of neural networks. 09Machine Learning Specialization (DeepLearning.AI)Andrew Ng's updated introduction to machine learning. 10Stanford CS229: Machine LearningStanford's graduate machine learning course. 11Mathematics for Deep LearningCalculus, probability and the maths behind deep learning. 12Linear Algebra for Deep LearningTensors, attention and LoRA explained. 13Probability Theory for Deep LearningLikelihood, softmax and KL divergence. 14Statistics for Deep LearningGeneralization and calibration.

Step 2: Core Deep Learning Courses

11 entries

Courses and books that teach neural networks from the ground up.

15Deep Learning Specialization (DeepLearning.AI)Specialization on neural networks, sequence models and more. 16Deep Learning (Coursera)Coursera listing of the deep learning specialization. 17fast.ai Practical Deep LearningFree, code-first deep learning course. 18Neural Networks: Zero to Hero (Karpathy)Video series building neural networks and language models from scratch. 19MIT Introduction to Deep LearningMIT's introductory deep learning course site. 20Dive into Deep LearningFree interactive book with code in PyTorch, JAX and TensorFlow. 21Understanding Deep LearningFree textbook on deep learning. 22Deep Learning (Goodfellow, Bengio, Courville)Free online textbook on deep learning. 23NYU Deep Learning (Spring 2021)Course materials from NYU's deep learning course. 24UvA Deep Learning tutorialsUniversity of Amsterdam tutorial notebooks on deep learning. 25UC Berkeley CS182Berkeley deep neural networks course site (2021).

Step 3: Vision, Language and Generative Models

14 entries

Specialise in computer vision, natural language processing, transformers and generative models.

26Stanford CS231nDeep learning for computer vision. 27Stanford CS224NNLP with deep learning. 28Stanford CS25: Transformers UnitedStanford seminar course on transformers. 29Stanford CS236: Deep Generative ModelsCourse on generative models, including diffusion and autoregressive models. 30Stanford CS330Deep multi-task and meta learning. 31Hugging Face LearnFree courses on NLP, LLMs and agents. 32Hugging Face LLM CourseFree course on working with large language models. 33Generative AI with LLMs (DeepLearning.AI)Course on how large language models are built and adapted. 34Jay Alammar's blogVisual explanations of transformers and other ML ideas. 35Steps to Build Powerful LLMsThe complete training pipeline for large language models. 36Fine-Tuning TechniquesLoRA, QLoRA, DPO and GRPO. 37Diffusion ModelsDiffusion models directory. 38Generative Adversarial NetworksGANs: papers, applications and code. 39Vision Language ModelsOpen and proprietary vision language models.

Frameworks and Tutorials

6 entries

Official tutorials for the main deep learning frameworks.

40PyTorch tutorialsOfficial PyTorch tutorials. 41TensorFlow tutorialsOfficial TensorFlow tutorials. 42Keras getting startedOfficial Keras introduction. 43JAX tutorialsOfficial JAX tutorials. 44Deep Learning FrameworksPyTorch, TensorFlow, JAX and Keras compared. 45Deep Learning PlatformsPlatforms for building and training deep learning models.

Evaluation and Deployment

5 entries

Measuring models and taking them to production.

46Made With MLFree course on designing and deploying ML applications. 47The Full Stack (Full Stack Deep Learning)Courses and resources for building ML products. 48Evaluation Metrics for Deep LearningClassification, detection and generation metrics. 49Deployment Strategies for Deep LearningServing and deploying deep learning models. 50MLOps PlatformsTop machine learning operations tools.

Certifications

8 entries

Vendor credentials relevant to deep learning work. Exam names and availability change, so check each provider's page.

51NVIDIA certification programsNVIDIA certification programs including generative AI and AI infrastructure. 52NVIDIA trainingNVIDIA training offerings, including Deep Learning Institute courses. 53AWS Certified Machine Learning Engineer – AssociateAssociate-level AWS credential for building and operating ML workloads. 54Professional Machine Learning Engineer (Google Cloud)Professional-level Google Cloud ML credential. 55Databricks Certified Machine Learning AssociateDatabricks credential for ML on its platform. 56TensorFlow Developer Certificate (closed)Google's page says the exam is closed while it evaluates the next step, and existing credentials stay valid for three years from the pass date. 57AI/ML CertificationsNine AI and ML exams compared. 58IT CertificationsIT certifications guide.

Practice, Papers and Community

9 entries

Practise on real problems and keep up with research.

59Kaggle competitionsMachine learning competitions for practice. 60arXiv: machine learningRecent machine learning preprints. 61Trending Papers (Hugging Face)Trending machine learning papers with code. 62NeurIPSTop machine learning conference. 63ICMLInternational Conference on Machine Learning. 64ICLRInternational Conference on Learning Representations. 65DistillArchive of clear, interactive ML articles. 66Data Sets for ML and DLUCI and other datasets for practice. 67Top Machine Learning JournalsRankings and impact factors.

Related Pages

6 entries

Neighbouring pages on this site.

68Machine LearningMachine learning hub. 69Free AI CoursesFree AI courses from top universities. 70Computer VisionComputer vision hub. 71Natural Language ProcessingNLP hub. 72LLM HubIndex of guides and directories on language models. 73Edge AIAI at the edge.

Frequently Asked Questions

How do I learn deep learning?

Build the maths and machine learning basics, then take a core course such as the DeepLearning.AI Deep Learning Specialization or fast.ai, implement models in PyTorch, specialise in vision, language or generative models, and practise on Kaggle or your own projects.

Which deep learning course should I start with?

For a code-first start, fast.ai Practical Deep Learning or Karpathy's Zero to Hero. For structured theory, the DeepLearning.AI Deep Learning Specialization, MIT's Introduction to Deep Learning or the free Dive into Deep Learning book. Choose by whether you prefer coding or theory first.

Is there a deep learning certification?

There is no single standard one. Options include NVIDIA's certification programs, the AWS Certified Machine Learning Engineer Associate, Google's Professional Machine Learning Engineer and the Databricks ML credentials. Google has closed the TensorFlow Developer Certificate exam, with existing credentials valid for three years from the pass date.

Should I learn PyTorch, TensorFlow or JAX?

Most current courses and research code use PyTorch, so it is a common first choice. Keras and TensorFlow remain widely used in production, and JAX is popular in research. The Deep Learning Frameworks page on this site compares them.

What maths do I need for deep learning?

Linear algebra, probability, statistics and basic calculus. See the Mathematics, Linear Algebra, Probability Theory and Statistics for Deep Learning pages on this site, plus Khan Academy and 3Blue1Brown.

How is this different from the Machine Learning courses page?

This guide focuses on neural networks, transformers and generative models, and on frameworks, deployment and NVIDIA and cloud credentials. The Machine Learning Courses and Certifications page covers classical machine learning, reinforcement learning and MLOps in more depth.

How current is this page?

Links were checked on 2 November 2026. Course editions, exam names, prices and availability change often, so confirm details on each provider's own page. Descriptions summarise each provider's pages and are not rankings.