What You’ll Learn: DL Fundamentals
You’ll go from understanding basic ML concepts to confidently building and training simple neural networks using fundamental principles.
Neural Network Theory
Learn how neurons, layers, and weights form the basis of deep learning models.
Backpropagation
Understand and implement the core algorithm for training neural networks.
Activation & Loss Functions
Explore and apply different functions to control network behavior and measure performance.
Common Architectures
Build and train basic feedforward and multi-layer perceptron (MLP) networks.
Who Is This Course For?
Ideal for developers and data scientists with basic ML knowledge who want to understand the core concepts of Deep Learning.
- Intermediate Python developers interested in DL
- Data scientists wanting to move beyond traditional ML
- Students preparing for advanced DL courses or research
Hands-On Projects
Binary Classifier from Scratch
Implement a simple neural network with one neuron to classify data.
Multi-Layer Perceptron
Build an MLP from scratch using NumPy to solve a multi-class classification problem.
Capstone
Regression Model
Train a neural network to predict continuous values, applying optimization techniques.
5-Week DL Syllabus
~50 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: NN Basics & Perceptrons
- Introduction to neural networks
- Perceptron algorithm
- Linear separability
- Implementing a single neuron
Week 2: Backpropagation Deep Dive
- Chain rule in the context of NNs
- Calculating gradients
- Weight and bias updates
- Implementing backpropagation for a simple network
Week 3: Activation & Loss Functions
- Common activation functions (Sigmoid, ReLU, Tanh)
- Loss functions (MSE, Cross-Entropy)
- Choosing the right functions for your task
- Implementing activation functions
Week 4: Common Architectures
- Feedforward networks (FFNs)
- Multi-Layer Perceptrons (MLPs)
- Building an MLP from scratch
- Forward pass implementation
Week 5: Optimization & Regularization
- Optimization algorithms (SGD, Adam)
- Learning rate scheduling
- Regularization techniques (L1, L2, Dropout)
- Capstone project: End-to-end model training
NSTC‑Accredited Certificate
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Frequently Asked Questions
Yes, a basic understanding of machine learning concepts (supervised/unsupervised learning), Python, and libraries like NumPy and Pandas is required. Familiarity with basic neural network concepts is helpful but not mandatory.
Yes, you will implement fundamental neural network components and algorithms (like backpropagation) from scratch in Python to solidify your understanding before using frameworks.
