Deep Learning Fundamentals

Deep learning basics: perceptron, activation functions, forward/backward propagation, and neural network architecture.

1. 1Deep Learning Fundamentals Part 1Free
2. 2Deep Learning Fundamentals Part 2
3. 3Deep Learning Fundamentals Part 3
4. 4Deep Learning Fundamentals Part 4
5. 5Deep Learning Fundamentals Part 5
6. 6Deep Learning Fundamentals Part 6

Introduction to TensorFlow and Keras: tensor operations, building sequential models, compile, fit, and evaluate.

1. 1TensorFlow Fundamentals Part 1Free
2. 2TensorFlow Fundamentals Part 2
3. 3TensorFlow Fundamentals Part 3
4. 4TensorFlow Fundamentals Part 4
5. 5TensorFlow Fundamentals Part 5
6. 6TensorFlow Fundamentals Part 6

Building neural networks for regression problems: architecture, loss functions, optimizers, and techniques to prevent overfitting.

1. 1Neural Network for Regression Part 1Free
2. 2Neural Network for Regression Part 2
3. 3Neural Network for Regression Part 3
4. 4Neural Network for Regression Part 4
5. 5Neural Network for Regression Part 5
6. 6Neural Network for Regression Part 6

Neural networks for classification: softmax, cross-entropy loss, multi-class classification, and deep learning model evaluation.

1. 1Neural Network for Classification Part 1Free
2. 2Neural Network for Classification Part 2
3. 3Neural Network for Classification Part 3
4. 4 scientify_content_template (3) scientify_content_template (3) 75% 10 F180 Neural Network for Classification Part 4 Summarise this data Neural Network for Classification Part 4 Turn on screen reader support To enable screen reader support, press ⌘+Option+Z To learn about keyboard shortcuts, press ⌘slashAllyvio Mahardika has joined the document.
5. 5Neural Network for Classification Part 5
6. 6Neural Network for Classification Part 6

Exercises and deep learning portfolio project: build end-to-end neural networks for regression and classification on real datasets.

1. 1Exercise & Project Portfolio 10