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If you’re interested in understanding the fundamentals of neural networks without relying on high-level libraries like TensorFlow or Keras, then the course 【NumPy・Python3で】ゼロから作るニューラルネットワーク is an excellent choice. This course offers a hands-on approach, guiding you through building neural networks from scratch using just NumPy and Pandas, which are core Python libraries for matrix calculations and data handling.
What makes this course stand out is its focus on core principles—covering concepts such as backpropagation, gradient descent, and the mathematical basis behind neural network optimization. The instructor explains these complex topics with clear, step-by-step explanations, making it accessible even if your math background is limited to middle school level. The course also includes sample code in Jupyter Notebook format, allowing you to experiment and understand the impact of parameters like learning rate and hidden layers.
I highly recommend this course for students and aspiring AI developers who wish to gain a solid understanding of neural network mechanics. While the mathematical content is explained thoroughly, it does require some basic comfort with concepts like exponents, logs, and derivatives. If you prefer visual learning through videos and are less interested in the mathematical underpinnings, this might not be the best fit.
Overall, this course is a fantastic resource for anyone who wants to deepen their understanding of neural networks and the mathematics behind deep learning, all while building practical skills in Python and NumPy.
Enroll Course: https://www.udemy.com/course/neuralnet/