Fundamentals of Deep Learning
Fundamentals of Deep Learning
What You'll Learn
- Build and train deep neural networks from scratch
- Master TensorFlow, PyTorch, and Keras frameworks
- Implement CNNs for image classification and computer vision
- Apply RNNs, LSTMs, and transformers for NLP tasks
- Understand transfer learning and pre-trained models
- Deploy deep learning models to production environments
- Apply generative models including GANs and diffusion models
- Complete an end-to-end deep learning capstone project
Course Content
Course Preview Video
Course Requirements
Basic Python programming knowledge and familiarity with machine learning concepts recommended. Understanding of linear algebra and calculus is helpful but not required.
- Level: Intermediate
- Delivery: Online / Self-paced
- Prerequisites: Python basics and ML fundamentals
- Certificate: Certificate of Completion upon successful completion
Frequently Asked Questions
Do I need machine learning experience?
Basic ML knowledge is helpful. We cover deep learning from fundamentals, but some prior exposure to Python and machine learning concepts will make the material easier to follow.
What deep learning topics are covered?
The course covers neural networks, CNNs, RNNs, transformers, GANs, diffusion models, transfer learning, and MLOps for deploying models to production.
Do I need a GPU?
We provide cloud GPU notebooks (Google Colab) for hands-on exercises, so no expensive hardware is required. Production deployment strategies are also covered.
Will I build real AI applications?
Yes, you'll build image classifiers, language models, and generative AI applications. The capstone project involves creating and deploying a complete deep learning solution.
How long is the course access?
You receive lifetime access to all course materials, including future updates on new architectures and frameworks.
Will I receive a certificate?
Yes, you will receive a Certificate of Completion after successfully completing the capstone project and all assessments.
Course Tutor
Dr. Sarah Johnson
Dr. Sarah Johnson is a Principal AI Research Scientist with over 15 years of experience in deep learning and neural networks. She has led AI research teams at Google DeepMind and Meta AI, where she contributed to groundbreaking work in computer vision and natural language processing. Dr. Johnson holds a PhD in Machine Learning from Stanford University and has published over 80 research papers in top-tier AI conferences including NeurIPS, ICML, and CVPR. She is a co-author of several widely-used open-source deep learning frameworks and is a frequent keynote speaker at major AI conferences worldwide.
Throughout this course, Sarah will guide you through cutting-edge deep learning techniques, hands-on coding exercises, and real-world AI applications that will prepare you for a career in artificial intelligence.
Course Reviews
Dr. Johnson's expertise shines through every module. The CNN and transformer sections are particularly strong. My understanding of deep learning has gone from basic to professional level.
The best deep learning course available. The hands-on PyTorch and TensorFlow exercises are incredibly practical. The capstone project landed me a job as an ML engineer.
As a data scientist, this course took my skills to the next level. The GANs and diffusion model modules are fascinating. Sarah's explanations are clear and engaging.
The transformer and BERT modules are exceptional. I now confidently work with modern NLP models. The MLOps section showed me how to deploy models at scale.
PhD-level content made accessible. The training optimization and regularization modules saved me weeks of trial and error. Worth every penny.
The course structure is brilliant, building from simple neural networks to cutting-edge architectures. The capstone project gave me a portfolio piece that impressed employers. 5 stars!