Welcome to our collection of deep learning project tutorials! Whether you're a beginner or an experienced machine learning practitioner, these tutorials will guide you through various deep learning projects.
Project 1: Image Classification
In this project, you will learn how to classify images using convolutional neural networks (CNNs). We will use a dataset like CIFAR-10 to train our model.
- Step 1: Install the required libraries (
tensorflow,numpy, etc.) - Step 2: Load and preprocess the CIFAR-10 dataset
- Step 3: Build and train the CNN model
- Step 4: Evaluate the model's performance
For more detailed instructions, check out our Image Classification Tutorial.
Project 2: Sentiment Analysis
Sentiment analysis is the process of determining whether a piece of text is positive, negative, or neutral. In this project, you will build a sentiment analysis model using recurrent neural networks (RNNs).
- Step 1: Install the required libraries (
tensorflow,numpy,pandas, etc.) - Step 2: Load and preprocess the dataset (e.g., IMDb reviews)
- Step 3: Build and train the RNN model
- Step 4: Analyze the sentiment of new texts
Read our Sentiment Analysis Tutorial for a comprehensive guide.
Project 3: Natural Language Processing (NLP)
In this project, you will build a language model using transformer-based architectures, such as BERT or GPT. We will use this model for various NLP tasks, like text classification and question answering.
- Step 1: Install the required libraries (
transformers,torch,torchtext, etc.) - Step 2: Load and preprocess the dataset (e.g., Wikipedia corpus)
- Step 3: Fine-tune the pre-trained model for your specific task
- Step 4: Evaluate the model's performance
To learn more, visit our NLP Tutorial.
If you're looking for more resources on deep learning, don't forget to check out our Deep Learning Blog.