Weekend Introduction to Deep Learning

Course Modules & Weekly Breakdown

Week 1 – Introduction to Deep Learning

What is Deep Learning? How it differs from Machine Learning

Real-world applications (vision, NLP, speech, gaming, etc.)

Setting up Python environment (TensorFlow/Keras, Jupyter Notebook, Colab)

Learning Outcome:

By the end of this week, students will be able to clearly distinguish between traditional Machine Learning and Deep Learning approaches, understand the wide range of real-world applications such as computer vision, NLP, and speech recognition, and successfully set up their working environment using TensorFlow/Keras with Jupyter Notebook or Google Colab.

Week 2 – Python & Math Refresher for DL

NumPy, Pandas review

Linear algebra basics for neural networks (vectors, matrices, dot products)

Activation functions (Sigmoid, ReLU, Tanh, Softmax)

Learning Outcome:

This week ensures that students strengthen their Python skills with NumPy and Pandas, while revisiting essential linear algebra concepts like vectors, matrices, and dot products that form the backbone of deep learning computations. They will also gain a solid grasp of activation functions, including Sigmoid, ReLU, Tanh, and Softmax, and understand their importance in building effective neural networks.

Week 3 – Neural Network Basics

Understanding neurons & layers

Forward propagation & loss functions

Building your first neural network in Keras

Learning Outcome:

By the end of this week, students will understand the structure of artificial neurons and layers, the process of forward propagation, and the role of different loss functions. They will also build their very first neural network using Keras, giving them hands-on experience with implementing a working model.

Week 4 – Training Neural Networks

Backpropagation explained

Optimizers (SGD, Adam, RMSProp)

Overfitting & regularization (Dropout, L2 regularization)

Learning Outcome:

Students will develop a strong understanding of how neural networks learn through backpropagation and gradient descent. They will explore different optimizers like SGD, Adam, and RMSProp, and learn how to prevent overfitting using techniques such as dropout and regularization.

Week 5 – Image Classification with Neural Networks

MNIST dataset (handwritten digits)

Building a classifier in Keras

Accuracy, confusion matrix, and evaluation

Learning Outcome:

By the end of this week, students will be able to apply neural networks to image classification tasks using the MNIST dataset. They will learn how to train, test, and evaluate models with metrics like accuracy and confusion matrices, gaining their first practical exposure to computer vision.

Week 6 – Convolutional Neural Networks (CNNs) – Part 1

Why CNNs for images? Convolution & pooling layers

Building a CNN for the MNIST or CIFAR-10 dataset

Learning Outcome:

This week introduces students to convolution and pooling operations, helping them understand why CNNs are particularly effective for image-related tasks. They will build their first CNN model for the MNIST or CIFAR-10 dataset.

Week 7 – Convolutional Neural Networks (CNNs) – Part 2

Advanced CNN architectures (VGG, ResNet overview)

Data augmentation for better results

Transfer Learning (using pretrained models)

Learning Outcome:

Students will explore advanced CNN architectures such as VGG and ResNet, learn how data augmentation improves performance, and apply transfer learning using pretrained models. By the end of this week, they will know how to enhance accuracy and efficiency for image-based projects.

Week 8 – Recurrent Neural Networks (RNNs)

Sequential data & time series basics

Introduction to RNNs & LSTMs

Text generation/sentiment analysis mini project

Learning Outcome:

This week equips students with the knowledge to handle sequential data and time series using RNNs and LSTMs. They will also work on a small text-based project, such as sentiment analysis or text generation, to understand how deep learning applies to natural language data.

Week 9 – Natural Language Processing with Deep Learning

Word embeddings (Word2Vec, GloVe)

Text classification with LSTMs/GRUs

Using Keras Tokenizer & Embedding layers

Learning Outcome:

By the end of this week, students will understand the role of word embeddings like Word2Vec and GloVe, and build text classification models using LSTMs or GRUs. They will also gain practical experience with the Keras Tokenizer and embedding layers for handling textual data.

Week 10 – Generative Models (Intro)

Autoencoders basics

Variational Autoencoders (high-level intro)

GANs (Generative Adversarial Networks) concept & examples

Learning Outcome:

This week introduces students to the world of generative AI. They will learn the fundamentals of autoencoders, variational autoencoders, and GANs (Generative Adversarial Networks), along with their practical applications such as image generation and anomaly detection.

Week 11 – End-to-End Deep Learning Project (Guided)

Choose dataset (image, text, or time series)

Preprocess data → Build model → Train → Evaluate

Visualize training results

Learning Outcome:

Students will work on a guided end-to-end project where they select a dataset, preprocess it, build and train a model, and evaluate results. This week emphasizes applying everything they’ve learned into a complete deep learning pipeline with visualized outcomes.

Week 12 – Final Project & Career Guidance

Students present their own deep learning projects

How to deploy models (TensorFlow Lite, simple Flask API demo)

Career paths: DL Engineer, Researcher, AI Developer

Freelancing & research opportunities

Learning Outcome:

In the final week, students will develop and present their own deep learning projects, showcasing their skills and creativity. They will also learn practical model deployment methods, such as TensorFlow Lite or Flask APIs, and explore career opportunities in deep learning, including roles in AI research, engineering, and freelancing.

Industry Trends Covered

• Computer Vision – Applications in facial recognition, self-driving cars, and medical imaging.
• Natural Language Processing (NLP) – Powering chatbots, virtual assistants, and sentiment analysis.
• Generative AI – Introduction to Autoencoders, VAEs, and GANs for creative design, data synthesis, and cybersecurity.
• Transfer Learning & Pretrained Models – Faster, more efficient model development using state-of-the-art architectures.
• Edge AI & Model Deployment – Running deep learning models on mobile, IoT, and lightweight devices.
• AI in Industry – Real-world case studies from healthcare, finance, e-commerce, and gaming.

Who Should Attend?

This course is ideal for:

• Students and beginners with basic Python knowledge who want to explore AI & Deep Learning
• Professionals looking to upskill in Machine Learning and Deep Learning technologies
• Freelancers aiming to offer AI, Computer Vision, or NLP services on global platforms
• Data enthusiasts and engineers interested in building AI-powered applications
• Anyone aspiring for careers as Deep Learning Engineers, AI Developers, or AI Researchers
• Tech enthusiasts curious about the latest advancements in Artificial Intelligence

Tools & Technologies You’ll Master

• Programming Language: Python (primary language for Deep Learning)
• Libraries & Frameworks: TensorFlow, Keras, PyTorch (optional), NumPy, Pandas, Matplotlib, Seaborn
• Development Environment: Jupyter Notebook, Google Colab (for cloud-based training), Anaconda
• Visualization Tools: Matplotlib & Seaborn for data and model performance visualization
• Version Control: Git & GitHub for managing and showcasing projects
• Deployment Tools: TensorFlow Lite & Flask (for basic model deployment demos)

Learning Outcomes

By the end of this course, you will:

• Gain a solid understanding of neural networks, CNNs, RNNs, and generative models.
• Build practical applications: image classification, text classification, NLP, and generative AI demos.
• Use TensorFlow/Keras to design, train, and evaluate deep learning models.
• Apply concepts like transfer learning, regularization, and optimization for better performance.
• Complete and present an end-to-end deep learning project for your GitHub portfolio.
• Explore career opportunities in Deep Learning Engineering, AI Development, Research, and Freelancing.