What You'll Learn
- Implement neural networks from scratch, including forward and backward propagation
- Master gradient descent, SGD with momentum, and other optimization techniques
- Build custom layers, activation functions, and loss functions without external libraries
- Apply your custom neural network to solve the Fashion-MNIST classification challenge
Requirements
- Basic knowledge of Python programming
- Familiarity with linear algebra concepts like vectors and matrices
- An interest in understanding neural networks at a fundamental level
Who This Course is For
- Beginners who want to understand how neural networks work under the hood
- Machine learning enthusiasts looking to deepen their knowledge through hands-on implementation
- Developers who want to build custom neural network models from scratch
- Students and professionals seeking to strengthen their grasp of core deep-learning concepts
Your Instructor
Nick Ovchinnikov
Software Engineer: Web, Machine Learning, Math
4.3 Instructor Rating
1,947 Reviews
145,561 Students
6 Courses
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