End-to-End: Automated Hyperparameter Tuning For Deep Neural Networks

In this video, I am going to show you how you can do #HyperparameterOptimization for a #NeuralNetwork automatically using Optuna. This is an end-to-end video in which I select a problem and design a neural network in #PyTorch and then I find the optimal number of layers, drop out, learning rate, and other parameters using Optuna. The dataset used in this video can be found here: https://www.kaggle.com/c/lish-moa Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) 00:00 Introduction 01:56 Dataset class 06:17 Start with train.py 08:19 Cross-validation folds 13:38 Reading the data 24:10 Engine 29:48 Model 35:10 Add model and engine to training 43:05 Optuna 49:02 Start tuning with Optuna 52:50 Training, suggestions and outro To buy my book, Approaching (Almost) Any Machine Learning problem, please visit: https://bit.ly/buyaaml Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek