30-day Python challenge for Deep learning. Each day focuses on a specific topic or task to help build a strong foundation in deep learning using Python.
Day 1: Introduction to Python
- Install Python and Jupyter Notebook
- Learn basic Python syntax (variables, data types, loops, conditionals)
Day 2: Python Libraries for Data Science
- Introduction to NumPy and Pandas
- Perform basic data manipulation and analysis
Day 3: Data Visualization
- Learn to use Matplotlib and Seaborn for data visualization
- Create basic plots (line, bar, scatter)
Day 4: Introduction to Machine Learning
- Understand the basics of machine learning
- Learn about supervised and unsupervised learning
Day 5: Linear Regression
- Implement a simple linear regression model using Scikit-Learn
- Evaluate the model's performance
Day 6: Classification
- Implement a logistic regression model using Scikit-Learn
- Evaluate the model's performance
Day 7: Data Preprocessing
- Learn about data preprocessing techniques (scaling, normalization, encoding)
- Apply preprocessing to a dataset
Day 8: Introduction to Neural Networks
- Understand the basics of neural networks
- Learn about neurons, activation functions, and layers
Day 9: Setting Up TensorFlow and Keras
- Install TensorFlow and Keras
- Understand the basic structure of a Keras model
Day 10: Building a Simple Neural Network
- Build and train a simple neural network on a small dataset (e.g., MNIST)
- Evaluate the model's performance
Day 11: Deep Learning Concepts
- Learn about key deep learning concepts (backpropagation, gradient descent)
- Understand the importance of loss functions and optimizers
Day 12: Improving Neural Networks
- Learn about techniques to improve neural networks (dropout, batch normalization)
- Implement these techniques in your model
Day 13: Convolutional Neural Networks (CNNs)
- Understand the basics of CNNs
- Build and train a simple CNN on an image dataset (e.g., CIFAR-10)
Day 14: Evaluating CNNs
- Evaluate the performance
- Use techniques like confusion matrix and classification report
Day 14: Evaluating CNNs (continued)
- Evaluate the performance of your CNN model
- Use techniques like confusion matrix and classification report
Day 15: Data Augmentation
- Learn about data augmentation techniques
- Apply data augmentation to your image dataset
Day 16: Transfer Learning
- Understand the concept of transfer learning
- Use a pre-trained model (e.g., VGG16, ResNet) for your image classification task
Day 17: Fine-Tuning Pre-trained Models
- Fine-tune a pre-trained model on your custom dataset
- Evaluate the performance of the fine-tuned model
Day 18: Recurrent Neural Networks (RNNs)
- Understand the basics of RNNs
- Build and train a simple RNN for a sequence prediction task
Day 19: Long Short-Term Memory (LSTM) Networks
- Learn about LSTM networks and their advantages over traditional RNNs
- Implement an LSTM network for a text generation task
Day 20: Natural Language Processing (NLP)
- Introduction to NLP and its applications
- Preprocess text data (tokenization, padding, etc.)
Day 21: Word Embeddings
- Learn about word embeddings (Word2Vec, GloVe)
- Use pre-trained word embeddings in your NLP model
Day 22: Sequence-to-Sequence Models
- Understand sequence-to-sequence models and their applications
- Build a simple sequence-to-sequence model for a translation task
Day 23: Attention Mechanism
- Learn about the attention mechanism in deep learning
- Implement attention in your sequence-to-sequence model
Day 24: Generative Adversarial Networks (GANs)
- Understand the basics of GANs
- Build and train a simple GAN for image generation
Day 25: Variational Autoencoders (VAEs)
- Learn about VAEs and their applications
- Implement a VAE for image generation
Day 26: Reinforcement Learning
- Introduction to reinforcement learning
- Implement a simple reinforcement learning algorithm (e.g., Q-learning)
Day 27: Deep Reinforcement Learning
- Learn about deep reinforcement learning
- Implement a deep Q-network (DQN) for a simple game
Day 28: Model Deployment
- Learn about model deployment techniques
- Deploy a trained model using Flask or FastAPI
Day 29: Model Optimization
- Learn about model optimization techniques (quantization, pruning)
- Apply optimization techniques to your trained model
Day 30: Capstone Project
- Choose a deep learning project of your interest (e.g., image classification, text generation, etc.)
- Apply the knowledge and techniques you have learned over the past 29 days to complete the project
- Document your project and share it on GitHub or a similar platform
By the end of this 30-day challenge, to give a solid understanding of deep learning concepts and practical experience with various deep learning models and techniques.