Keras Tutorial
Keras high-level neural networks APIs that provide easy and efficient design and training of deep learning models. It is built on top of powerful frameworks like TensorFlow, making it both highly flexible and accessible. Keras has a simple and user-friendly interface, making it ideal for both beginners and experts in deep learning.
Keras simplifies the process of building and training deep learning models while abstracting away complex underlying operations. This tutorial covers everything you need to know to get started with Keras, from installation to advanced topics, making it a perfect guide for those looking to dive into deep learning
Installing Keras
Keras is simple to install as part of TensorFlow. You can install it using pip, making it easy to get started with deep learning models right away. This section will guide you through installation steps on various operating systems.
Introduction to Keras
Keras is a high-level neural networks API designed to simplify the process of building and training deep learning models. It’s built on top of powerful frameworks like TensorFlow and provides an easy-to-use interface, making it accessible to both beginners and experts
- Introduction
- Datasets
- How to create Models
- Sequential and Functional API
- Keras Layers
- Keras Input Layer
- Convolution Layer
Training the Model with Keras
Training a model in Keras involves preparing your data, defining a model and specifying the number of epochs. Keras simplifies the training process with built-in methods for monitoring performance, adjusting hyperparameters and saving the trained model.
- Save the Final Model
- Saving a Deep Learning model
- Epochs to Train a Neural Network
- to Create a Custom Loss Function
- Log Keras Loss Output
Neural Network with Keras
Building a neural network in Keras involves selecting appropriate layers, defining activation functions and tuning the model’s hyperparameters. With Keras, you can design models for a wide range of tasks, including image classification and regression.
- Neural Style Transfer
- Training a Neural Network
- Building an Auto-Encoder
- Deep Convolutional GAN
- Colorization Autoencoders
- Swish Function
GAN with Keras
Generative Adversarial Networks (GANs) can be easily built with Keras. GANs consist of two models a generator and a discriminator and are used for generating new data. Keras provides a simple way to implement these networks for tasks like image generation.
Evaluating the Model
Evaluating a model in Keras involves testing its performance using unseen data. Functions like model.evaluate() and model.predict() allow you to assess the accuracy and efficiency of your model while making predictions on new inputs.
- Model evaluate and predict
- Loss Function and Metric
- Hyperparameter tuning
- Transfer learning and fine-tuning
- Dropout
Miscellaneous Topics in Keras
Keras offers many advanced features such as transfer learning, custom loss functions and data augmentation. These additional techniques enhance model performance and provide flexibility for more complex tasks like time series forecasting or image processing.
- Image Processing
- Time Series Forecasting with LSTMs
- Keras vs Tensorflow vs Pytorch
- Differences Between Scikit Learn, Keras and PyTorch
- keras with Scikit-Learn
R Language with Keras
Keras can be used with R to build deep learning models. The keras package in R allows users to access Keras functionalities, making it easy to build, train and evaluate neural networks using R’s ecosystem.
- Install Package Keras
- Introduction to keras
- Convert TensorFlow Tensor to R Array
- Build a Neural Network Classifier
- Train and Test Neural Networks
- Image Classification
- class weight in Keras Package
- Custom Loss Function
- Early stopping
- Optimizing for Accuracy
Projects of Keras
Explore hands-on Keras projects like fashion classification and traffic sign recognition to apply your learning in real-world scenarios.
Keras Quiz
Test your understanding of Keras concepts and deep learning with an interactive quiz covering key topics.
