Back

Neural Networks with Keras

Explore deep learning through Keras, an intuitive library based on TensorFlow.

Explore courses
Register Now
Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Explore deep learning through Keras, an intuitive library based on TensorFlow.

  2. Engage in practical exercises to construct and train neural networks.

  3. Gain skills to tackle intricate challenges such as image classification, natural language processing, and predictive modeling.

CERTIFICATION:

  1. Achieve a Certified Deep Learning Practitioner with Keras credential, demonstrating your proficiency in designing, training, and optimizing neural networks.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the core principles of neural networks and deep learning.

  2. Create and execute feedforward, convolutional, and recurrent neural networks with Keras.

  3. Prepare and enhance data for effective model training.

  4. Improve model efficiency through hyperparameter optimization and regularization methods.

  5. Assess and analyze model predictions to guarantee strong performance.

Enroll Now

Course Curriculum

Introduction to Neural Networks and Keras
  1. What are Neural Networks?
    • Overview of neural networks: architecture, layers, and activation functions.
    • Key concepts: weights, biases, forward propagation, and backpropagation.
  2. Introduction to Keras
    • History of Keras and its role in TensorFlow.
    • Benefits of Keras: simplicity, modularity, and extensibility.
Fundamentals of Neural Networks
  1. Types of Neural Networks
    • Feedforward Neural Networks (FNNs).
    • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
  2. Activation Functions
    • Sigmoid, ReLU, Tanh, and Softmax.
  3. Loss Functions
    • Mean squared error, cross-entropy, and hinge loss.
  4. Optimization Techniques
    • Gradient descent, Adam, RMSprop, and SGD.
Setting Up the Environment
  1. Installing Keras and TensorFlow
    • Step-by-step guide to setting up the environment.
    • Introduction to Google Colab for free GPU resources.
  2. Keras Basics
    • Sequential vs. Functional API.
    • Creating and compiling a simple neural network.
Building Neural Networks with Keras
  1. Input, Hidden, and Output Layers
    • Designing architectures based on the problem.
    • Adding, stacking, and configuring layers.
  2. Model Compilation
    • Choosing optimizers, loss functions, and metrics.
  3. Training the Model
    • Feeding data using .fit(), batching, and epochs.
  4. Evaluating the Model
    • Metrics like accuracy, precision, recall, and F1-score.
Working with Real-World Data
  1. Data Preparation
    • Data cleaning, normalization, and augmentation.
    • Splitting data into training, validation, and test sets.
  2. Case Study: Image Classification
    • Building a neural network to classify images using the MNIST or CIFAR-10 dataset.
  3. Case Study: Regression Problem
    • Predicting house prices using tabular data.
Advanced Neural Network Architectures
  1. Convolutional Neural Networks (CNNs)
    • Filters, pooling, and strides.
    • Applications: image recognition and object detection.
  2. Recurrent Neural Networks (RNNs)
    • Basics of sequence modeling.
    • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).
  3. Autoencoders
    • Encoding and decoding data for dimensionality reduction.
Hyperparameter Tuning
  1. Optimizing Neural Networks
    • Learning rate, number of layers, and neurons.
  2. Grid Search and Random Search
    • Tuning with Keras Tuner and Scikit-learn.
  3. Dropout and Regularization
    • Preventing overfitting in large models.
Deploying Neural Networks
  1. Saving and Loading Models
    • Using Keras .h5 and TensorFlow SavedModel formats.
  2. Deploying to Production
    • Serving models using TensorFlow Serving or Flask APIs.
  3. Model Monitoring
    • Real-time monitoring of deployed models.
Capstone Project
  1. End-to-End Neural Network Project
    • Build a complete neural network for a specific problem (e.g., image classification, sentiment analysis, or time-series prediction).
    • Data preprocessing, model building, hyperparameter tuning, and deployment.
  2. Example: Build a CNN for classifying handwritten digits or an RNN for stock price prediction.

Training Features

Hands-On Learning

Real-world datasets and step-by-step coding exercises.

Keras Functional API

Build complex architectures using Keras Functional API.

Advanced Techniques

Transfer learning, fine-tuning, and working with pre-trained models.

Interactive Tools

Use TensorBoard for visualizing training metrics and model architecture.

End-to-End Projects

From data preparation to model deployment.

Certification

A professional certificate validating expertise in building neural networks with Keras.

Get in Touch

    Our Relevant Courses list