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DL Hands On

  Deep Learning (DL) Hands On Fashion MNIST Classifier : Classify images of clothing using a simple Convolutional Neural Network (CNN). This is a great next step after the classic MNIST handwritten digit dataset. Cat vs. Dog Image Classifier : Use a pre-trained model with transfer learning (e.g., VGG16, ResNet50) to classify images of cats and dogs. This teaches you how to leverage existing, powerful models. Simple Generative Adversarial Network (GAN) : Build a basic GAN to generate new images of handwritten digits. This introduces the concept of a generator and a discriminator model competing against each other. Time Series Forecasting with LSTMs : Predict future stock prices, weather patterns, or sales data using a Long Short-Term Memory (LSTM) network. Autoencoder for Anomaly Detection : Train an autoencoder on a normal dataset (e.g., valid credit card transactions). When a new, anomalous data point is fed to it, the reconstruction error will be high, indicating a potential ano...
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Reinforcement Learning with a Deep Q-Network (DQN)

Reinforcement Learning with a Deep Q-Network (DQN) Reinforcement Learning with a Deep Q-Network (DQN) A step-by-step guide to training an agent to play a game using a Deep Q-Network (DQN) . Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The core deep learning framework for building the Q-network. gym: An environment library for reinforcement learning, used here for the 'CartPole' game. numpy: Used for numerical operations. collections: Provides `deque` for creating a replay buffer. ...
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Music Genre Classifier

Music Genre Classifier Music Genre Classifier A step-by-step guide to classifying music genres using Spectrograms and a CNN . Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The core deep learning framework. numpy: Used for numerical operations. librosa: A Python library for audio analysis, used here to extract audio features and create spectrograms. matplotlib: A plotting library for visualizing the spectrograms. 1. Data Loading and Feature Extraction ...
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Simple Transformer for Text Classification

Simple Transformer for Text Classification Simple Transformer for Text Classification A step-by-step guide to using a Transformer for a basic text classification task. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The core deep learning framework. numpy: Used for numerical operations. tensorflow_text: Provides tools for text preprocessing. 1. Data Loading and Preprocessing What the code does: It uses the `IMDB` movie review dataset. The text is tokenized and padded to a fixed l...
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Multi-label Image Classification

Multi-label Image Classification Multi-label Image Classification A step-by-step guide to building a model that can predict multiple labels for a single image. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The deep learning framework for building the CNN model. numpy: Used for numerical operations and handling one-hot encoded labels. pandas: Used to read and process the CSV file containing labels. sklearn: Provides `train_test_split` for data splitting. 1. Data Loading and Preprocessing ...
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Text Generation with a Character-level RNN

Text Generation with a Character-level RNN Text Generation with a Character-level RNN A step-by-step guide to building a model that generates new text one character at a time using a Recurrent Neural Network (RNN) . Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The core deep learning framework for building the RNN model. numpy: Used for numerical operations. os: Used for file system operations. 1. Data Loading and Preprocessing What the code does: It downloads a text file (Shake...
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Autoencoder for Anomaly Detection

Autoencoder for Anomaly Detection Autoencoder for Anomaly Detection A step-by-step guide to building an autoencoder to identify anomalies in a dataset. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The deep learning framework for building the autoencoder model. numpy: Used for numerical operations. matplotlib: A plotting library for visualizing the results. sklearn: Used to split the dataset and for the `MinMaxScaler`. 1. Data Loading and Preprocessing ...
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