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Deep Learning & Neural Networks Specialization
4.9
Offered by DeepLearning.AISpecializationAccredited
Deep Learning & Neural Networks Specialization
Master modern deep learning foundations from multi-layer perceptrons, backpropagation, and Adam optimization to CNN computer vision and Transformer self-attention.
4.9(147,244 ratings)
•1,000,135 already enrolled
Instructor: Andrew Ng
Founder & CEO, DeepLearning.AI · Stanford University / DeepLearning.AI
Curriculum Overview
About This Course
Master modern deep learning foundations from multi-layer perceptrons, backpropagation, and Adam optimization to CNN computer vision and Transformer self-attention.
Throughout this track, you will transition from core conceptual mental models to building hardened, real-world software. Every module combines structured conceptual explanations with live in-browser coding katas, architectural diagrams, and automated test assertions. By completing the hands-on milestones, you build a verified portfolio demonstrating deep competency to engineering leaders and top employers.
Interactive In-Browser Katas
Write, compile, and debug real code in our integrated sandbox with instant test verification and syntax hints.
Production Architecture
Explore real enterprise patterns, memory trade-offs, concurrency paradigms, and scalable system design.
Verifiable Certificate
Earn an accredited, shareable digital credential authenticated by DeepLearning.AI and ASCI Institute.
Portfolio Capstone Project
Build and deploy an end-to-end applied capstone deliverable ready to showcase on your GitHub and resume.
Curriculum Highlights & Architectural Focus
- Vectorized Forward & Backward Propagation without External Frameworks
- Advanced Optimization: Adam, RMSprop, Momentum, and Learning Rate Decay
- Computer Vision Architectures: ResNet Residual Blocks and YOLO Object Detection
- Natural Language Modeling: Recurrent Memory, LSTMs & Scaled Dot-Product Attention
- Multi-GPU Distributed Training Strategies & Mixed Precision (FP16/BF16)
Technologies, Frameworks & Tooling Covered
Python 3.12PyTorchNumPyTensorFlowTransformersCUDA
Course Specifications
Offered ByDeepLearning.AI
CredentialSpecialization
Skill LevelAdvanced
Curriculum4 Modules · 5 Lessons
Commitment16 Weeks · Self-Paced
InstructionEnglish · Auto Subtitles & Transcripts
CertificateIncluded upon Completion
Free Audit Available · Full Access Included with Plus
Who Should Take This Course?
Ideal for students, software engineers, and technical professionals aiming to master Deep Learning & Neural Networks Specialization, pass technical interviews, and build production-grade applications.
Official Partner Masterclass
Official Course Video & Keynote
Official Course Video:DeepLearning.AI
Click to Watch Official Lecture
1080p HDDeep Learning & Neural Networks Specialization
Official Lecture & WalkthroughKey Lecture Chapters & Timestamps
What you will learn
Key competencies verified by DeepLearning.AI
Build and train deep neural networks, identify key architecture parameters, and implement vectorized forward and back-propagation
Analyze bias and variance, implement dropout, Xavier/He weight initialization, and batch normalization
Master optimization algorithms: Momentum, RMSprop, Adam, and learning rate decay schedules
Build state-of-the-art CNNs for computer vision and sequence models (LSTMs, GRUs, Transformers) for NLP
Skills you will gain
Deep LearningConvolutional Neural Networks (CNN)Recurrent Neural Networks (RNN)TensorFlowHyperparameter TuningTransformers
Shareable Certificate
Earn a certificate upon completion backed by DeepLearning.AI.
100% Online & Self-Paced
Start instantly and learn at your own schedule from any device.
Hands-on Challenge Katas
Write, compile, and debug real code in the browser IDE.
Flexible Deadlines
Reset deadlines according to your personal availability.
Syllabus: What is in this Specialization
4 Modules · 5 Lessons · 5 Capstone Projects
•
1
Module 1: Deep Neural Network Foundations & Backprop
Derive gradient descent, matrix calculus, and backpropagation for L-layer deep nets.
2
Module 2: Hyperparameter Tuning, Regularization & Adam
Prevent overfitting with L2 weight decay, Dropout, Batch Normalization, and Adam.
3
Module 3: Convolutional Neural Networks (CNNs)
Filter kernels, padding, striding, Max-Pooling, and ResNet skip connections.
4
Module 4: Transformers & Scaled Dot-Product Attention
Self-attention mechanism: $\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$.
Capstone Applied Learning Project
End-to-End Neural Style Transfer & Autonomous Vision Pipeline
Construct a multi-layer deep convolutional network with residual connections, implement custom loss functions for neural style transfer, and optimize deployment latency.
Final Deliverable
Jupyter notebooks, trained TensorFlow model checkpoints, and live web demo.
Technologies & Stack
PythonTensorFlow 2NumPyMatplotlibGPU Compute
Instructors
Learn from industry pioneers and senior educators from DeepLearning.AI
Andrew Ng
Founder & CEO, DeepLearning.AI
Stanford University / DeepLearning.AI
Founding Lead of Google Brain, and Adjunct Professor of Computer Science at Stanford University.
91%
Positive Career Outcome
of learners reported tangible career benefits including promotions, new employment, or research publications.
Median reported compensation: ₹35,00,000 / year
Learner Reviews
4.9 out of 5(147,244 ratings)
January 2026
"Andrew Ng explains backpropagation better than any textbook in existence. The vectorized Python exercises build real intuition."
David ChenDeep Learning Engineer at NVIDIA
Verified LearnerFrequently Asked Questions
Answers to common queries about certificates, grading, and prerequisites