What will you learn in Make Your Own Neural Network in Python Course
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Understand the mathematical foundation behind neural networks.
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Implement a basic neural network from scratch using only Python and NumPy.
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Learn how forward propagation, backpropagation, and weight updates work.
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Train a neural network to classify handwritten digits from the MNIST dataset.
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Gain practical knowledge of activation functions, learning rates, and error metrics.
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Build foundational skills to transition into deep learning and AI frameworks like TensorFlow or PyTorch.
Program Overview
Module 1: Introduction to Neural Networks
1.5 hours
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Topics: Biological vs. artificial neurons, history and significance of neural networks.
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Hands-on: Visualize how data is transformed in layers of a simple network.
Module 2: Math Behind Neural Nets
2 hours
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Topics: Matrix operations, dot product, sigmoid function, and gradient descent.
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Hands-on: Manually compute forward and backward passes with NumPy.
Module 3: Forward Propagation
1.5 hours
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Topics: Input to hidden layer to output transformations, activation functions.
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Hands-on: Code a single-layer neural network with Python arrays.
Module 4: Backpropagation and Weight Updates
2.5 hours
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Topics: Loss functions, delta rule, partial derivatives, learning rate.
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Hands-on: Implement backpropagation to optimize the network’s weights.
Module 5: MNIST Dataset Classification
3 hours
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Topics: Preprocessing images, feeding real data into a neural net.
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Hands-on: Build a working digit recognizer using your own neural network.
Module 6: Tuning and Optimization
2 hours
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Topics: Hyperparameters, performance tracking, epochs, overfitting basics.
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Hands-on: Adjust learning rates, hidden units, and layers to improve accuracy.
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Job Outlook
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Neural networks form the backbone of deep learning, powering AI in healthcare, finance, and more.
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Skills in neural net fundamentals are highly valuable for roles in machine learning and AI engineering.
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Excellent stepping stone for advanced frameworks like TensorFlow, Keras, or PyTorch.
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Prepares learners for roles like data scientist, ML engineer, AI researcher, or algorithm developer.
Explore More Learning Paths
Advance your deep learning and neural network skills with these carefully selected courses designed to help you build, train, and deploy AI models using Python and popular frameworks.
Related Courses
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Convolutional Neural Networks in TensorFlow Course – Learn to design and implement CNNs for image recognition and computer vision applications.
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Introduction to Deep Learning & Neural Networks with Keras Course – Explore deep learning fundamentals and build neural networks using Keras.
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Introduction to Neural Networks and PyTorch Course – Gain hands-on experience in creating and training neural networks using PyTorch.
Related Reading
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What Is Python Used For – Understand how Python supports AI, deep learning, and neural network development across various industries.
