Deep Learning Made Simple: Neural Networks to AI

Name: Deep Learning Made Simple: Neural Networks to AI
Rating: 4.4 (14 reviews)

Learn Neural Networks, CNNs, Training, and AI Systems with Visual Explanations and Hands-On Examples

Created bySuman Kunwar

Last updated 7/2026

English

English [Auto],

What you'll learn

Develop a clear understanding of neural network architecture and functionality
Explain the role of weights, biases, and learning processes in model training
Understand core deep learning concepts, including Convolutional Neural Networks (CNNs) and optimization techniques
Apply foundational deep learning concepts to real-world problem scenarios
Establish a strong conceptual foundation for further study in artificial intelligence and machine learning

Course content

1 section • 12 lectures • 37m total length

Neural Networks Fundamentals3:07
Explore how neural networks, inspired by the brain, use artificial neurons, weights, and bias across input, hidden, and output layers to learn patterns in deep learning through training and backpropagation.
How Neural Networks Learn3:06
Explore how neural networks learn by measuring error with loss functions, using gradient descent to update weights, and applying backpropagation in the training loop.
Activation Functions in Neural Networks2:15
Explore how activation functions introduce nonlinearity in neural networks, enabling learning of complex patterns in images, speech, and text with sigmoid, ReLU, and softmax in hidden layers to produce probabilities.
Convolutional Neural Networks (CNNs)2:13
Explain how convolutional neural networks use small filters to scan image regions, build feature maps from detected edges and textures, and apply pooling to reduce data for final predictions.
Receptive Field & Output Dimensions1:40
Learn how receptive fields in convolutional neural networks determine what the network sees and how stride, padding, filters, and kernel size set the output dimensions.
Training Deep Learning Models2:33
Train deep learning models by feeding labeled data and updating weights through backpropagation and gradient descent across epochs and mini-batches to improve accuracy and prevent overfitting.
Model Optimization Techniques1:49
Apply model optimization techniques to prevent overfitting and improve generalization in neural networks, including regularization, dropout, data augmentation, early stopping, and hyperparameter tuning.
Regularization Example Code5:14
Compare regularized and unregularized neural networks with L2 regularization and dropout using numpy, matplotlib, and TensorFlow Keras, training 500 epochs on noisy data and visualizing losses, predictions, and validation errors.
Hyperparameter Tuning Example Code6:33
Tune hyperparameters with grid search for a TensorFlow Keras dense network on fashion mnist, with data normalized to 0-1; train, evaluate, and select the best model for testing.
Training Comparison Example Code4:09
Compare CNN models on CIFAR-10 using TensorFlow Keras with optional L2 regularization and dropout, and evaluate the impact of data augmentation via ImageDataGenerator on training, validation, and test accuracy.
How Modern AI Systems Work2:02
Learn how neural networks, CNNs, and training optimize modern AI systems from data to deployment, including preprocessing, model selection, backpropagation, validation, metrics, and iterative improvement with feedback.
Using ONNX for Deploying Deep Learning Models3:15
Neural Networks Quick Check

Requirements

A basic understanding of mathematics, particularly algebra
Familiarity with fundamental programming concepts is beneficial but not mandatory
An interest in artificial intelligence and machine learning
A commitment to learning and engaging with course materials

Description

This course contains the use of artificial intelligence.

Deep learning is at the core of today’s most advanced artificial intelligence systems, enabling breakthroughs in computer vision, natural language processing, and intelligent automation.

This course provides a comprehensive and structured introduction to deep learning, designed to build a strong conceptual foundation while maintaining practical relevance. It systematically guides learners from the fundamentals of neural networks to the development of modern AI systems.

You will begin by exploring the biological inspiration behind neural networks and understanding how artificial neurons are structured and connected. The course then progresses into key components such as weights, biases, activation functions, and the role they play in learning complex patterns from data.

As the course advances, you will gain insight into the training process, including backpropagation and optimization techniques that enable models to improve performance. You will also explore specialized architectures, such as Convolutional Neural Networks (CNNs), widely used in image recognition and computer vision tasks.

In addition, the course covers essential strategies for improving model performance, including regularization, dropout, and data augmentation. Finally, it provides a holistic view of how deep learning models are integrated into real-world applications, bridging the gap between theory and practice.

This course emphasizes clarity, structured learning, and conceptual understanding, making it suitable for learners seeking a solid foundation in deep learning as well as professionals aiming to strengthen their understanding of modern AI systems.

Who this course is for:

Individuals seeking a structured introduction to deep learning
Students and professionals aiming to build foundational knowledge in artificial intelligence
Software developers interested in expanding into machine learning and AI domains
Learners who prefer a conceptual and visually driven approach to understanding complex topics