Learn Artificial Neural Network From Scratch in Python

Resumen

Embark on a comprehensive journey through the realm of artificial neural networks (ANN) with our in-depth Python course. This course is meticulously designed to provide you with the most extensive insights into neural network theory, coupled with hands-on experience in coding using Python and Numpy.

What You Will Learn:

• How to code a neural network from scratch using Python and Numpy.
• The underlying mathematical principles of neural networks.
• A solid understanding of Artificial Neural Networks (ANN) and Deep Learning fundamentals.
• The derivation of the backpropagation rule from first principles.
• Key neural network concepts such as activation, backpropagation, and feedforward mechanisms.
• Techniques to evaluate and optimize neural network models.

Dive into this course if you aim to master deep learning techniques and wish to apply neural network models to solve real-world business problems.

Whether you are new to machine learning or looking to expand your expertise in data science, this course will empower you with the knowledge and skills to develop advanced neural network models in Python.

Course Highlights:

• An introduction to Python basics to get you started on your coding journey.
• A deep dive into the theoretical concepts behind neural networks.
• Hands-on experience in creating regression and classification ANN models in Python.
• A detailed tutorial on backpropagation with numerical examples.

By the end of this course offered by Udemy, you will gain the confidence to create your first artificial neural network using deep learning techniques. This course will set you on the path to becoming proficient in deep learning, making you capable of tackling complex data science problems.

Categories:

Python Courses, Machine Learning Courses, Deep Learning Courses, Neural Networks Courses, Data Science Courses, Numpy Courses.

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Impartido por

Sachin Kafle

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Materias