Welcome to the exercises section for the "Deep Learning 101" course. Here, you will find a variety of exercises designed to help you practice and reinforce the concepts covered in the course.

Exercises Overview

Introduction to Neural Networks

In this section, you will learn the basics of neural networks and how they work. Read more about neural networks.

Example Exercise

  1. Task: Draw a simple neural network diagram.
  2. Resources: Use online resources or textbooks to help you.
  3. Submit: Post your diagram in the comments section below.

Training Neural Networks

Understanding how to train neural networks is crucial for successful deep learning projects. Learn more about training neural networks.

Example Exercise

  1. Task: Implement a simple neural network in Python using TensorFlow or PyTorch.
  2. Resources: Check out the official documentation for TensorFlow or PyTorch.
  3. Submit: Share your code in the comments section below.

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are powerful for image recognition tasks. Explore CNNs further.

Example Exercise

  1. Task: Train a CNN to classify images of cats and dogs.
  2. Resources: Use the CIFAR-10 dataset for this exercise.
  3. Submit: Describe your approach and results in the comments section below.

Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are great for sequence data like time series or text. Read about RNNs here.

Example Exercise

  1. Task: Implement an RNN to predict the next word in a sentence.
  2. Resources: Utilize the PTB dataset for this exercise.
  3. Submit: Share your findings and any challenges you faced in the comments section below.

Natural Language Processing

Natural Language Processing (NLP) is a key area in deep learning. Learn more about NLP.

Example Exercise

  1. Task: Build a sentiment analysis model using a pre-trained BERT model.
  2. Resources: Use the Hugging Face Transformers library.
  3. Submit: Discuss your model's performance and any adjustments you made in the comments section below.

Good luck with your exercises, and feel free to ask questions or seek help in the comments section! 🌟