Architecting Intelligence: The Fundamentals of Neural Networks Training Course

Introduction

Neural networks are at the heart of the most transformative technologies of our time, from image recognition and natural language processing to predictive modeling and autonomous systems. Inspired by the human brain, these sophisticated computational models have revolutionized the field of artificial intelligence, enabling machines to learn and solve complex problems with remarkable accuracy. This course provides a comprehensive and practical introduction to the core principles, architectures, and applications of neural networks, empowering you to build a strong foundation in this essential area of modern machine learning.

This five-day training program will guide you from the basic building blocks of a single neuron to the design and training of multi-layered neural networks. You will gain a deep understanding of concepts like activation functions, backpropagation, and optimization algorithms, while working with popular frameworks to implement and experiment with different network types. By the end of this course, you will not only comprehend how neural networks work but also be equipped with the skills to design, train, and evaluate your own models for a variety of real-world applications.

Duration 5 days

Target Audience This course is for data scientists, machine learning engineers, software developers, and researchers who want to gain a strong foundational understanding of neural networks, from theory to practical implementation.

Objectives

  • To understand the biological and mathematical inspiration behind neural networks.
  • To build a fundamental understanding of the core components of a single neuron.
  • To differentiate between various types of activation functions and their uses.
  • To grasp the concept of forward and backpropagation for training a network.
  • To understand how to implement a basic neural network from scratch using Python.
  • To explore common neural network architectures, including Multi-Layer Perceptrons (MLPs).
  • To learn about optimization algorithms and techniques for improving model performance.
  • To recognize and solve common challenges in neural network training, such as overfitting.
  • To use a modern deep learning framework to build and train a neural network.
  • To gain a foundational understanding of the applications of neural networks in different domains.

Course Modules

Module 1: Introduction to Neural Networks

  • A brief history of neural networks and their evolution.
  • The biological inspiration: neurons and synapses.
  • The basic structure of a single perceptron.
  • Understanding weights, biases, and weighted sums.
  • The role of activation functions.

Module 2: Activation Functions

  • The purpose of non-linear activation functions.
  • Exploring popular functions: Sigmoid, ReLU, Tanh.
  • Understanding the vanishing and exploding gradient problems.
  • Choosing the right activation function for a hidden layer.
  • The importance of the final layer's activation function.

Module 3: Building a Simple Neural Network

  • Understanding the architecture of a Multi-Layer Perceptron (MLP).
  • The concept of hidden layers.
  • Implementing a simple feedforward network.
  • Setting up the network structure and initializing parameters.
  • The forward pass: making a prediction.

Module 4: Loss Functions and Backpropagation

  • The purpose of a loss function in measuring error.
  • Popular loss functions for regression and classification.
  • The core idea of backpropagation.
  • Calculating gradients with the chain rule.
  • Updating weights and biases to reduce loss.

Module 5: Optimization Algorithms

  • The challenge of finding the best weights.
  • An overview of Gradient Descent.
  • Understanding Stochastic Gradient Descent (SGD) and Mini-batch Gradient Descent.
  • Introduction to advanced optimizers like Adam and RMSprop.
  • How learning rate affects the training process.

Module 6: Training and Evaluation

  • The importance of separating data into training, validation, and test sets.
  • Monitoring training and validation loss curves.
  • Understanding overfitting and underfitting.
  • Techniques for preventing overfitting, like regularization.
  • Evaluating model performance using metrics like accuracy, precision, and recall.

Module 7: Deep Neural Networks

  • The concept of "deep" learning with many hidden layers.
  • Advantages and disadvantages of deep networks.
  • The importance of proper weight initialization.
  • Using dropout to prevent co-adaptation of neurons.
  • Strategies for hyperparameter tuning.

Module 8: Convolutional Neural Networks (CNNs)

  • An introduction to neural networks for image processing.
  • Understanding convolution and pooling layers.
  • The importance of feature extraction.
  • Building a basic CNN for image classification.
  • Applications of CNNs in computer vision.

Module 9: Recurrent Neural Networks (RNNs)

  • An introduction to neural networks for sequential data.
  • Understanding the concept of a hidden state.
  • The challenges of training traditional RNNs.
  • An overview of LSTMs and GRUs.
  • Applications of RNNs in natural language processing.

Module 10: Introduction to a Framework

  • An overview of a popular framework like TensorFlow or PyTorch.
  • Setting up the development environment.
  • The high-level API for building and training models.
  • The benefits of using a framework over coding from scratch.
  • A practical hands-on session building a model with the framework.

Module 11: Transfer Learning

  • The concept of using a pre-trained model.
  • Why transfer learning is a powerful technique.
  • Fine-tuning pre-trained models for new tasks.
  • The benefits of reducing training time and data requirements.
  • Practical examples of transfer learning in image and text domains.

Module 12: Ethical Considerations

  • The importance of ethical AI development.
  • Understanding and mitigating algorithmic bias.
  • Data privacy and security in neural network applications.
  • The societal impact of advanced AI.
  • Building a responsible AI development mindset.

Module 13: The Future of Neural Networks

  • Current research and emerging trends in the field.
  • An overview of Generative Adversarial Networks (GANs).
  • The role of transformers in modern NLP.
  • The concept of reinforcement learning and its applications.
  • A roadmap for continued learning and specialization.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Architecting Intelligence: The Fundamentals Of Neural Networks training Course in Namibia
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