Course · Intermediate
Network Architectures
This course is the second course in a series of courses that will take you from no knowledge of deep learning to training your own GPT model (Generative Pre-trained Transformer).
- Intermediate friendly
- 0 hrs
- 3 lessons
- Free
Course overview
Build and train neural network architectures, including dense and recurrent models, to solve classification and sequence prediction problems.
What's inside
3 lessons
- 01 7 minDense Networks
<p><span style="color: rgb(36,41,47);background-color: rgb(255,255,255);font-size: 16px;">Dense networks are the basic form of a neural network, where every input is connected to an output. These can also be called fully connected networks.</span> In this lesson, you'll learn how to build a dense neural network from scratch using Python.</p>
- 02 120 minClassification with Neural Networks
<p><span style="color: rgb(31,35,40);background-color: rgb(255,255,255);font-size: 16px;">In this lesson, you'll learn how to train neural networks for classification tasks. Classification is how we get neural networks to categorize data for us. Classification is used by language models like GPT to predict the next word in a sequence. </span></p>
- 03 120 minRecurrent Networks
<p>Learn how to use neural networks to take in sequences of input and make predictions. A sequence of input can be a sentence made up of words, or a series of weather observations.</p>
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