Unleashing AI: A Foundational Course in Deep Learning Training Course

Introduction

Deep learning, a powerful subset of machine learning, is at the forefront of the artificial intelligence revolution, driving breakthroughs in everything from autonomous vehicles to natural language processing and medical imaging. By using multi-layered neural networks, deep learning models are capable of learning complex patterns and representations from vast amounts of data, often outperforming traditional methods. This training course offers a comprehensive and accessible entry point into this exciting field.

Over the five-day program, you will gain a solid understanding of the core concepts, architectures, and practical applications of deep learning. We will move beyond the theory to build and train neural networks from scratch using popular frameworks, providing you with the hands-on experience needed to start your own deep learning projects. By the end, you'll be well-prepared to tackle real-world challenges and continue your journey as a deep learning practitioner.

Duration 5 days

Target Audience This course is for data scientists, machine learning engineers, and developers who have a good grasp of Python and foundational machine learning concepts, and are eager to specialize in deep learning.

Objectives

  • To understand the fundamental principles and components of a neural network.
  • To learn how to build and train a basic feed-forward neural network.
  • To grasp the concepts of activation functions and loss functions.
  • To understand the backpropagation algorithm and its role in training.
  • To explore different types of neural networks, including CNNs and RNNs.
  • To learn how to use a deep learning framework like TensorFlow or PyTorch.
  • To address common challenges like overfitting and vanishing gradients.
  • To apply deep learning to solve problems in computer vision and NLP.
  • To use best practices for hyperparameter tuning and model optimization.
  • To gain hands-on experience with a final deep learning project.

Course Modules

Module 1: The Building Blocks of Neural Networks

  • From neurons to nodes: a conceptual introduction.
  • The structure of a neural network: layers and connections.
  • The role of weights, biases, and activation functions.
  • The difference between a single-layer and a multi-layer perceptron.
  • A simple example of how a neural network learns.

Module 2: Training a Neural Network

  • The concept of forward propagation.
  • Understanding the role of a loss function.
  • The backpropagation algorithm explained intuitively.
  • The role of optimizers like Gradient Descent and Adam.
  • The importance of epochs, batch size, and learning rate.

Module 3: Foundations in Code

  • Setting up a deep learning environment.
  • Introduction to a deep learning framework (e.g., TensorFlow/Keras).
  • Building and compiling a simple neural network.
  • Training the model and monitoring performance.
  • Making predictions with the trained model.

Module 4: Convolutional Neural Networks (CNNs)

  • The motivation for CNNs in computer vision.
  • Key concepts: convolution, pooling, and filters.
  • Understanding the architecture of a typical CNN.
  • Building a CNN for a simple image classification task.
  • Transfer learning as a powerful technique.

Module 5: Recurrent Neural Networks (RNNs)

  • The need for RNNs for sequential data.
  • The concept of a recurrent layer and hidden state.
  • The challenge of vanishing and exploding gradients.
  • An introduction to LSTMs and GRUs.
  • Building a simple RNN for text generation.

Module 6: Regularization and Overfitting

  • The problem of a model memorizing training data.
  • Common regularization techniques: Dropout and L1/L2 regularization.
  • The importance of validation and test sets.
  • Early stopping as a method to prevent overfitting.
  • A practical guide to implementing these techniques.

Module 7: Advanced Architectures

  • An overview of various state-of-the-art architectures.
  • Understanding transformers for natural language processing.
  • Introduction to Generative Adversarial Networks (GANs).
  • The concept of autoencoders for dimensionality reduction.
  • Practical use cases for these advanced models.

Module 8: Hyperparameter Tuning

  • The most important hyperparameters in deep learning.
  • Strategies for hyperparameter tuning.
  • Using a grid search or random search.
  • The role of a validation set in tuning.
  • Best practices for model optimization.

Module 9: Working with Data

  • The importance of data preprocessing and normalization.
  • Handling different data types for deep learning.
  • Data augmentation for computer vision tasks.
  • Using generators to handle large datasets.
  • Techniques for preparing data for NLP tasks.

Module 10: Computer Vision Applications

  • A deep dive into image classification.
  • Object detection and semantic segmentation.
  • Using deep learning for medical image analysis.
  • Building a real-world computer vision project.
  • The ethical considerations of computer vision.

Module 11: Natural Language Processing (NLP) Applications

  • A comprehensive guide to text classification.
  • Understanding machine translation with deep learning.
  • Using deep learning for sentiment analysis.
  • The role of word embeddings and tokenization.
  • Practical examples and a hands-on NLP project.

Module 12: Frameworks and Tools

  • A detailed comparison of TensorFlow and PyTorch.
  • The ecosystem of tools for deep learning.
  • Using tools for visualization like TensorBoard.
  • The importance of GPU computing for deep learning.
  • Deploying a deep learning model.

Module 13: The Future of AI

  • Emerging trends in deep learning research.
  • The role of deep learning in reinforcement learning.
  • The potential of deep learning in robotics and automation.
  • A final review of course objectives.
  • Resources for continued learning and research.

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

 

 

Unleashing Ai: A Foundational Course In Deep Learning training Course in Namibia
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