Foundational Courses
Foundational
Deep Learning
Provide a solid foundation in deep learning by introducing the essential concepts
5
14 enrolled students
Objective
Provide a solid foundation in deep learning by introducing the essential concepts, architecture, and applications of neural networks.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1. Basics of Deep Learning (3 hours)
Objective:
Introduce learners to the fundamentals of deep learning, including its history, evolution, and applications in various industries.
Topics:
- What is Deep Learning?
- History and Evolution of Neural Networks
- Differences between Machine Learning and Deep Learning
- Applications of Deep Learning in various industries
Hands-on Exercise:
Explore Deep Learning Applications through Case Studies
- Research and present at least two real-world applications of deep learning, such as image classification in healthcare or NLP for chatbots.
- Compare traditional machine learning methods versus deep learning approaches for these applications.
- Create a brief report or presentation summarizing these case studies, focusing on how deep learning improves performance and efficiency.
Module 2. Neural Networks (3 hours)
Objective:
Dive into the structure of neural networks, including the components of a neuron, different types of neural networks, and activation functions.
Topics Covered:
Structure of a Neuron: Input, Weights, Bias, Activation Function
- Types of Neural Networks: Feedforward, Recurrent, Convolutional
- Building a Neural Network from Scratch
- Activation Functions: Sigmoid, Tanh, ReLU
Hands-on Exercise:
Build a Simple Feedforward Neural Network from Scratch
- Implement a simple feedforward neural network from scratch using Python (without using deep learning libraries) for a basic classification task like the MNIST dataset.
- Manually define the forward propagation and backpropagation steps, including the implementation of activation functions (ReLU, Sigmoid).
- Evaluate the model on the test set and compute accuracy.
Module 3. Training Neural Networks (3 hours)
Objective:
Understand the training process of neural networks, including forward propagation, backpropagation, gradient descent, and solutions to overfitting.
Topics Covered:
Data Preparation and Normalization
- Forward Propagation and Backpropagation
- Cost Function and Gradient Descent
- Overfitting and Underfitting: Solutions and Prevention (Regularization, Dropout)
Hands-on Exercise:
MNIST handwritten digits.
- Apply techniques like data normalization and regularization (L2 regularization, dropout) to prevent overfitting.
- Visualize the training and validation loss over epochs, and observe the effect of regularization on model performance.
- Use gradient descent to optimize the model and experiment with different learning rates.
Module 4. Introduction to TensorFlow
Objective:
Familiarize learners with two of the most popular deep learning frameworks, TensorFlow and PyTorch, focusing on their core features and differences.
Topics:
- Installing TensorFlow and setting up the environment
- Basic TensorFlow operations and building simple neural networks
- Practical: Implement a neural network for a basic classification task using
- TensorFlow
Hands-on Exercise:
Implement a Neural Network for Classifying Fashion MNIST in TensorFlow
- Install TensorFlow and set up the environment.
- Load and preprocess the Fashion MNIST dataset (images of clothing items).
- Build and train a simple feedforward neural network using TensorFlow to classify the images into 10 categories.
- Evaluate model performance and tune hyperparameters (e.g., learning rate, batch size).
Module 5. Introduction to PyTorch
Topics:
Installing PyTorch and setting up the environment
- Understanding basic PyTorch operations (tensors, autograd, etc.)
- Building simple neural networks in PyTorch
Hands-on Exercise:
Implement a Neural Network for Image Classification using PyTorch
- Install PyTorch and set up your development environment.
- Load and preprocess the CIFAR-10 dataset (a set of 60,000 images in 10 classes).
- Implement a simple convolutional neural network (CNN) in PyTorch and train it for image classification.
- Visualize training and validation accuracy, and experiment with different model architectures.
- The environment.
Module 6: Capstone Project
Objective:
Apply all learned techniques in a real-world RL project, such as optimizing a robotic control system or an inventory management scenario.
Project:
Create an RL agent to solve a real-world problem, such as robotic control optimization or inventory management.
Hands-on Exercise:
Choose a real-world problem where reinforcement learning can be applied, such as:
- Robotic Control Optimization:
Use RL to train an agent to control a robotic arm for tasks like picking and placing objects or solving a block-stacking problem. Use simulators like OpenAI’s Gym or MuJoCo to train and test your agent. Implement techniques like PPO, DDPG, or TRPO to solve the task efficiently.
Frequently Asked Questions
1. What is the Foundational Deep Learning course about?
This course provides a comprehensive introduction to deep learning, covering the fundamental concepts, techniques, and tools used to build and train deep neural networks. You will learn about neural networks, backpropagation, activation functions, and explore key architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
2. What will I learn in this course?
You will learn how to build, train, and evaluate deep neural networks using frameworks like TensorFlow and Keras. Topics include supervised learning, gradient descent, loss functions, model regularization, CNNs, RNNs, and transfer learning. You will also get hands-on experience with deep learning projects.
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