Intro to Deep Learning
This is the course page for the Course Intro to Deep Learning. Please also enroll in Kaggle Learn Intro to Deep Learning course to complete the exercises. Also watch the lectures where we have added our own explanations.
| Topic | Description | Video Link |
|---|---|---|
| 01- single neuron | This video introduces the concept of the simplest neural network, the single neuron | Single Neuron |
| 01-exercise single neuron | This video explains step by step the exercise on the single neuron. Gain more clarity by doing this exercise. | Exercise Single Neuron |
| 02-deep neural network | This video explains a simple deep neural network , activation functions , hidden layers | Deep Neural Network |
| 02- exercise deep neural network | This video explains the step by step exercise on simple deep neural network , activation functions , hidden layers | Exercise Deep Neural Network |
| 03 - Stochastic Gradient Descent | This video explains the concept of optimizer , loss function , stochastic gradient descent | Stochastic Gradient Descent |
| 03 - exercise Stochastic Gradient Descent | In this exercise we will train a neural network on the Fuel Economy dataset and then explore the effect of the learning rate and batch size on SGD | Exercise Stochastic Gradient Descent |
| 04 - Overfitting and Underfitting | This video explains the concept of Overfitting and Underfitting | Overfitting and Underfitting |
| 04 - exercise Overfitting and Underfitting | In this exercise, we will learn how to improve training outcomes by including an early stopping callback to prevent overfitting | Exercise Overfitting and Underfitting |
| 05 - Dropout and BatchNormalization | This video explains the concept of Dropout and BatchNormalization to prevent overfitting and help in making the training more stable | Dropout and BatchNormalization |
| 05 - exercise Dropout and BatchNormalization | In this exercise, we will add dropout to the Spotify model from Exercise 4 and see how batch normalization can let us successfully train models on difficult datasets. | Exercise Dropout and BatchNormalization |
| 06 - Binary Classification | Binary Classification using deep neural networks | Binary Classification |
| 06 - exercise Binary Classification | In this exercise, we will build a model to predict hotel cancellations with a binary classifier. | Exercise Binary Classification |
| 07 - Red Wine Deep Learning Model | We use all the concepts learnt in the course to evaluate the Quality of the Red Wine using a Deep Learning Model. The concepts of Dropout , Batch Normalization , Early Stopping is demonstrated here | Red Wine Deep Learning Model |
| 08 - Streamlit UI with Red Wine Deep Learning Model | We use Streamlit to make the UI for the deep learning model | Streamlit UI with Red Wine Deep Learning Model |