Training/Testing on our Data - Deep Learning with Neural Networks and TensorFlow part 7

TL;DR

This video is part seven of a tutorial series on deep learning with TensorFlow neural networks and Python, focusing on running sentiment analysis through a deep neural network.

Transcript

what is going on everybody and welcome to part seven of our deep learning with tensorflow neural networks and Python tutorial Series in the last video what we did was we actually created this sentiment set. pickle just in case we actually wanted to just load it from a pickle probably in this one we'll just straight up use the function that created ... Read More

Key Insights

• 😫 Creating a sentiment feature set pickle can save time in future analysis by allowing for easy loading of the preprocessed data.
• 😫 Modifying code based on the specific data set is necessary for accurate analysis.
• 😫 Increasing the size of the data set is crucial for better accuracy in deep learning models.
• 😫 Handling and processing large data sets can be challenging and may require different techniques such as buffering.
• 😫 Training with larger data sets may take a significant amount of time.
• 😒 Saving the model as it trains can be useful for future use.
• 😀 Beyond deep neural networks, other approaches may be needed depending on the specific challenges faced.

Questions & Answers

Q: What is the purpose of the sentiment feature set pickle?

The sentiment feature set pickle is created to save time and avoid running the data preprocessing step every time. It allows for easy loading of the feature set in the future.

Q: How does the code need to be modified to fit the sentiment data set?

Some parts of the code, such as the number of classes and the size of the input layer, need to be changed according to the sentiment data set. Functions for creating the feature set and labels also need to be imported.

Q: Why is increasing the size of the data set important for better accuracy?

Neural networks perform better with larger data sets as they require a large amount of varied data to learn and make accurate predictions. Increasing the data set size can lead to improved accuracy.

Q: What challenges are faced with larger data sets?

When dealing with larger data sets, it becomes difficult to store all the data in memory. This requires using buffering or similar techniques to handle the data. Training also takes longer with larger data sets.

Summary & Key Takeaways

• The video discusses creating a sentiment feature set pickle and using it to train a deep neural network.

• The code provided is modified from the original to fit the sentiment data set.

• The accuracy of the sentiment analysis is around 58.7% with a small data set, and the video discusses the need for larger data sets for better results.