Unlocking Intelligence: A Beginner's Guide to Neural Networks Training Course

Introduction

Neural networks have revolutionized the field of machine learning, enabling us to solve complex problems in areas like computer vision, natural language processing, and predictive analytics. Inspired by the human brain, these powerful algorithms are at the heart of modern artificial intelligence. This course is your entry point into this fascinating and high-demand field. We will demystify the core concepts of neural networks and provide a solid foundation for your journey into deep learning.

Designed for complete beginners, this five-day program will guide you through the fundamental building blocks of neural networks, from the basic perceptron to multi-layered architectures. We'll focus on the intuition behind how these models learn, covering essential topics like activation functions, backpropagation, and optimization. You'll gain hands-on experience by building your first neural network from scratch, giving you the confidence to tackle more advanced topics in the future.

Duration 5 days

Target Audience This course is for students, developers, and aspiring data scientists with basic programming skills who are new to the field of machine learning and deep learning. No prior knowledge of neural networks is required.

Objectives

  • To understand the fundamental components of a neural network: neurons, layers, and connections.
  • To grasp the concept of an activation function and its role.
  • To learn how a neural network learns through forward and backpropagation.
  • To understand different types of neural networks, including feedforward and convolutional.
  • To gain practical experience building a simple neural network from scratch.
  • To implement a neural network using a popular deep learning framework like TensorFlow or PyTorch.
  • To recognize the importance of data preprocessing and handling.
  • To understand the challenges of overfitting and underfitting in neural networks.
  • To evaluate the performance of a trained neural network.
  • To be able to identify and troubleshoot common issues in network training.

Course Modules

Module 1: The Building Blocks

  • The neuron: the fundamental unit of a neural network.
  • How a single neuron processes information.
  • The role of weights, biases, and inputs.
  • The concept of layers: input, hidden, and output.
  • A visual and conceptual introduction to a basic network.

Module 2: The Perceptron and Activation Functions

  • The history and importance of the perceptron.
  • The "on/off" switch: the step function.
  • The need for non-linearity.
  • An overview of common activation functions (sigmoid, ReLU, tanh).
  • When to use which activation function.

Module 3: Training a Neural Network

  • The learning process: forward and backward passes.
  • What is a loss function?
  • The intuition behind backpropagation.
  • Using gradients to update weights and biases.
  • An analogy of a neural network "learning" to walk.

Module 4: Optimization and Regularization

  • How to get to the minimum: Gradient Descent.
  • The impact of learning rate.
  • An introduction to different optimizers (SGD, Adam, RMSprop).
  • What is overfitting and how to prevent it?
  • Regularization techniques: Dropout and L1/L2.

Module 5: Your First Neural Network

  • Setting up your development environment.
  • The data: a simple classification or regression problem.
  • Building a basic feedforward neural network from scratch.
  • A guided walkthrough of the code.
  • Training and evaluating your network.

Module 6: Introduction to TensorFlow/PyTorch

  • Why use a deep learning framework?
  • An overview of the TensorFlow or PyTorch ecosystem.
  • A comparison of the two frameworks.
  • Building your first model using a high-level API.
  • A simple example of training a model.

Module 7: The Data Journey

  • The importance of clean and prepared data.
  • Common data preprocessing techniques.
  • Handling categorical data.
  • The crucial step of data splitting (train/test).
  • A discussion on feature scaling.

Module 8: Beyond the Basics: CNNs

  • An introduction to convolutional neural networks (CNNs).
  • The concept of a convolution filter.
  • The role of pooling layers.
  • A simple visual explanation of how CNNs work.
  • A brief overview of applications like image recognition.

Module 9: Evaluating Performance

  • The difference between accuracy, precision, and recall.
  • The importance of the confusion matrix.
  • Understanding and using the validation set.
  • How to spot overfitting and underfitting.
  • Visualizing the training process.

Module 10: Hands-on Project

  • A guided project on a real-world dataset.
  • Applying all the concepts learned so far.
  • The process of iterating and improving your model.
  • Presenting and interpreting your results.
  • A discussion on the project's limitations.

Module 11: Common Issues and Troubleshooting

  • When your network doesn't learn: what to check first.
  • A discussion on vanishing and exploding gradients.
  • The role of batch size and learning rate.
  • Debugging your model's predictions.
  • Best practices for model training.

Module 12: Looking Ahead: Recurrent Networks

  • A conceptual overview of recurrent neural networks (RNNs).
  • The idea of a network with memory.
  • A brief discussion on LSTMs and GRUs.
  • A quick look at applications like text generation.
  • The future of neural networks.

Module 13: Next Steps in Deep Learning

  • Where to go after this course.
  • An overview of more advanced topics: GANs, Transformers.
  • Recommended resources for continued learning.
  • The role of neural networks in the industry.
  • Final Q&A and course wrap-up.

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

 

 

Unlocking Intelligence: A Beginner's Guide To Neural Networks Training Course in Namibia
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