Keras Training
Introduction to Keras
Gain an overview of Keras, a high-level neural networks API written in Python and capable of running on top of TensorFlow, Theano, or CNTK. Learn about its key features, simplicity, and ease of use for building deep learning models.
Getting Started with Keras
Learn how to get started with Keras, including installation, initial setup, and basic configuration. Understand how to create your first neural network model using Keras and run it using different backend engines.
Keras Models and Layers
Explore the different types of models available in Keras, such as the Sequential model and the Functional API. Learn how to build complex architectures using layers and how to customize them for specific tasks.
Data Preprocessing and Augmentation
Discover how to preprocess data for training neural networks in Keras. Learn about data normalization, tokenization, and augmentation techniques to enhance model performance and generalization.
Training and Evaluation
Understand the process of training neural networks using Keras. Learn about setting up training loops, monitoring performance, using callbacks, and evaluating models with metrics like accuracy and loss.
Advanced Keras Features
Dive into advanced features of Keras, such as custom layers, custom loss functions, and callbacks. Learn how to leverage these features to fine-tune your models and improve their performance.
Deploying Keras Models
Learn how to deploy Keras models to production. Understand the options for saving and loading models, exporting them for different platforms, and integrating them into applications or services.
Hands-On Labs and Practical Projects
Engage in hands-on labs and practical projects to apply your knowledge of Keras. Work on real-world deep learning problems, from image classification to natural language processing, using Keras.
Keras syllabus
Basic Overview of Keras
- Understanding the features of Keras
- Advantages of Keras
- Keras Limitations
Keras Installation and API
Installing Keras
- Installation of dependencies
- Installation of Theano
- Installation of TensorFlow
- Installation of Keras
- Testing each Installation
Configuring Keras
- Installation of Keras on Docker
- Installation of Keras on Google Cloud ML
- Installation of Keras on Amazon AWS
- Installation of Keras on Microsoft Azure
Keras API
- Basic Architecture of Keras
- Overview of predefined neural network layers
- Overview of predefined activation functions
- Understanding Loss Functions
- Understanding Metrics
- Some Useful Operations
Overview of Deep Learning with ConvNets
- Deep Convolutional Neural Network
- Understanding Deep Convolutional Neural Network (DCNN)
- Simple Example of DCNN
- Recognizing CIFAR-10 images with DL
Concept of Generative Adversarial Networks and WaveNet
- Overview of GAN
- Keras adversarial GANs for forging MNIST
- Keras adversarial GANs for forging CIFAR
- Understanding WaveNet
Word Embeddings
- Distributed Representations
- Understanding word2vec
- GloVe Exploring functionalities
- Using pre-trained embeddings
Overview of Recurrent Neural Networks (RNN)
- Basics of SimpleRNN cells
- Understanding RNN Topologies
- Using Gated Recurrent Unit (GRU)
- Concept of Bidirectional RNNs
- Vanishing and exploding gradients
- Using Long Short Term Memory (LSTM)
- Understanding Stateful RNNs
Additional Deep Learning Models
- Dealing with Keras Functional API
- Understanding Regression Networks
- Concept of Unsupervised Learning
- Keras Customization scenarios
- Using Generative Models
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details