Deep Learning Fundamentals
Unlock the power of neural networks. Build, train, and evaluate deep learning models with PyTorch — covering forward/backprop, CNNs, RNNs, and regularisation techniques.
What You'll Learn
Understand how artificial neural networks learn via forward and backward propagation
Build deep networks from scratch with PyTorch tensors and autograd
Design and train CNNs for image classification tasks
Apply RNNs and LSTMs to sequential data problems
Combat overfitting with dropout, batch normalisation, and data augmentation
Use transfer learning to achieve state-of-the-art results with minimal data
Curriculum Breakdown
Learning Format
Video Lessons
High-quality recorded lessons you can watch at your own pace.
Hands-on Projects
Real-world projects that reinforce every concept you learn.
Certificate
Earn a verifiable certificate upon successful completion.
Certification Details
Deep Learning Fundamentals Certificate
Issued by Tech101
Validate your deep learning skills with this advanced professional credential.
Certificate Requirements
- Shareable on LinkedIn
- PDF download
- Unique verification ID
- Graded assessment included
Completion Certificate
Awarded upon finishing all course content and submitting projects. Shows dedication and completion.
Graded Certificate
Earned by passing the final assessment with 70%+ score. Demonstrates verified skill proficiency.
Your Instructor
Sarah Johnson
Data Scientist & ML Engineer
Sarah has 10 years of experience in data science and machine learning, previously at Google AI and DeepMind.
Requirements & Prerequisites
Technical Requirements
- Machine Learning Fundamentals and Supervised Learning courses
- Python, NumPy, and Pandas proficiency
- A GPU (Google Colab free tier is sufficient)
Who This Course Is For
- ML practitioners ready to advance into deep learning
- Data scientists wanting to work with image, text, or audio data
- Data Science & AI track students
Student Reviews
Frequently Asked Questions
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