TensorFlow & Neural Networks

Build and train your first neural network with hands-on coding

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Course Curriculum

Build real neural networks with TensorFlow through hands-on projects

1

TensorFlow Basics & Setup

Get started with TensorFlow and Keras. Learn about tensors, operations, and set up your development environment for neural network projects.

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2

Data Preprocessing & MNIST

Load and preprocess the famous MNIST handwritten digits dataset. Learn data normalization, reshaping, and visualization techniques.

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3

Building Your First Neural Network

Create a simple feedforward neural network using Keras. Understand layers, activation functions, and model compilation.

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4

Training & Optimization

Train your neural network and understand the training process. Learn about loss functions, optimizers, and monitoring training progress.

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5

Model Evaluation & Improvement

Evaluate your model's performance using various metrics. Learn techniques to improve accuracy and prevent overfitting.

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6

Convolutional Networks & Deployment

Build a CNN for improved MNIST performance and learn how to save, load, and deploy your trained models.

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💻 Ready to build neural networks? Each lesson includes complete code examples and exercises!

What You'll Build

  • Complete MNIST digit classifier (98%+ accuracy)
  • Custom neural network architectures
  • Data preprocessing pipelines
  • Model evaluation and optimization workflows
  • Convolutional neural network for image recognition
  • Model deployment and inference system

Hands-On Neural Network Development

This practical course takes you from TensorFlow basics to building and deploying complete neural network models. You'll work with real datasets, implement different architectures, and learn industry best practices.

By the end of this course, you'll have built several neural networks from scratch, including a complete MNIST digit classifier, and understand how to optimize and deploy models in production environments.

Prerequisites

  • Completed AI/ML Fundamentals course (or equivalent knowledge)
  • Python programming experience
  • Basic understanding of neural networks
  • Familiarity with NumPy and basic data manipulation

Technologies You'll Use

  • TensorFlow 2.x: Primary deep learning framework
  • Keras: High-level API for neural networks
  • Python: Programming language
  • NumPy & Pandas: Data manipulation and analysis
  • Matplotlib: Data visualization
  • Jupyter Notebooks: Interactive development environment
6 Hands-on Lessons
5+ Coding Projects
4-5 Hours of Content
3 Datasets