Deep Learning
Overview
Course Objective
• Understand the intuition behind Artificial Neural Networks
• Apply Artificial Neural Networks in practice
• Understand the intuition behind Convolutional Neural Networks
• Apply Convolutional Neural Networks in practice
• Understand the intuition behind Recurrent Neural Networks
• Apply Recurrent Neural Networks in practice
• Understand the intuition behind Self-Organizing Maps
• Apply Self-Organizing Maps in practice
• Understand the intuition behind Boltzmann Machines
• Apply Boltzmann Machines in practice
• Understand the intuition behind AutoEncoders
• Apply AutoEncoders in practice
Who Should Attend
• Anyone interested in Deep Learning
• Students who have at least high school knowledge in math and who want to start learning
Deep Learning
• Any intermediate level people who know the basics of Machine Learning or Deep
Learning, including the classical algorithms like linear regression or logistic regression
and more advanced topics like Artificial Neural Networks, but who want to learn more
about it and explore all the different fields of Deep Learning
• Anyone who is not that comfortable with coding but who is interested in Deep Learning
and wants to apply it easily on datasets
• Any students in college who want to start a career in Data Science
• Any data analysts who want to level up in Deep Learning
• Any people who are not satisfied with their job and who want to become a Data Scientist
• Any people who want to create added value to their business by using powerful Deep
Learning tools
• Any business owners who want to understand how to leverage the Exponential
technology of Deep Learning in their business
• Any Entrepreneur who wants to create disruption in an industry using the most cutting
edge Deep Learning algorithms
Prerequisites
• Basic high school mathematics
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - What is Deep Learning and what are Neural Networks?
• Deep Learning as a branch of AI
• Neural networks and their history and relationship to neurons
• Creating a neural network in Python
Module 2
Artificial Neural Networks (ANN) Intuition
• Understanding the neuron and neuroscience
• The activation function (utility function or loss function)
• How do NN’s work?
• How do NN’s learn?
• Gradient descent
• Stochastic Gradient descent
• Backpropagation
Building an ANN
• Getting the python libraries
• Constructing ANN
• Using the bank customer churn dataset
• Predicting if customer will leave or not
Module 3
Evaluating Performance of an ANN
• Evaluating the ANN
• Improving the ANN
• Tuning the ANN
Hands-On Exercise
• Participants will be asked to build the ANN from the previous
exercise
• Participants will be asked to improve the accuracy of their ANN
Module 4 - Convolutional Neural Networks (CNN) Intuition
• What are CNN’s?
• Convolution operation
• ReLU Layer
• Pooling
• Flattening
• Full Connection
• Softmax and Cross-entropy
Module 5 - Building a CNN
• Getting the python libraries
• Constructing a CNN
• Using the Image classification dataset
• Predicting the class of an image
Module 6 - Evaluating Performance of a CNN
• Evaluating the CNN
• Improving the CNN
• Tuning the CNN
Module 7 - Hands-On Exercise
• Participants will be asked to build the CNN from the previous
exercise
• Participants will be asked to improve the accuracy of their CNN
Module 8 - Recurrent Neural Networks (RNN) Intuition
• What are RNN’s?
• Vanishing Gradient problem
• LSTMs
• Practical intuition
• LSTM variations
Module 9 - Building a RNN
• Getting the python libraries
• Constructing RNN
• Using the stock prediction dataset
• Predicting stock price
Module 10
Evaluating Performance of a RNN
• Evaluating the RNN
• Improving the RNN
• Tuning the RNN
Hands-On Exercise
• Participants will be asked to build the RNN from the previous
exercise
• Participants will be asked to improve the accuracy of their RNN
Module 11 - Self-Organizing Maps (SOM) Intuition – Day 3
• What are SOMs?
• K-means clustering
• How do SOMs learn?
• Reading Advanced SOMs
Module 12 - Building a SOM
• Getting the python libraries
• Constructing SOM
• Using the fraud detection dataset
• Predicting fraud
FAQs
General Questions:
Q: What is the Deep Learning course about?
This 3-day course introduces participants to Deep Learning, a core area of Artificial Intelligence that powers technologies like self-driving cars, medical diagnosis systems, and advanced game-playing bots. Through practical modules and real-life datasets, participants will explore and apply various neural network architectures, including CNNs, RNNs, SOMs, Boltzmann Machines, and AutoEncoders.
Q: Who should attend this course?
Anyone interested in Deep Learning
Students with high school math knowledge who want to dive into AI
Intermediate learners with basic ML knowledge (e.g., linear/logistic regression)
Individuals curious about Deep Learning but not yet confident with coding
College students looking to build a career in Data Science
Data analysts wanting to level up their skills
Professionals aiming for a career shift into Data Science
Business owners and entrepreneurs wanting to harness Deep Learning for innovation
Q: What are the prerequisites for this course?
Participants should have basic high school-level mathematics. Prior experience with Machine Learning or coding is helpful but not required.
Q: How long is the course?
The course spans 3 days.
Q: What key topics are covered in this course?
Introduction to Deep Learning and neural networks
Artificial Neural Networks (ANN): theory, building, tuning, evaluation
Convolutional Neural Networks (CNN): intuition, hands-on exercises
Recurrent Neural Networks (RNN): LSTMs and stock prediction projects
Self-Organizing Maps (SOM) for clustering and fraud detection
Boltzmann Machines and recommendation systems
AutoEncoders and denoising techniques
Hands-on exercises for each neural network type
Q: Will I receive a certification after completing the course?
Yes, participants will receive a certificate of completion, recognizing their understanding and hands-on application of Deep Learning techniques.
Program Content & Skills:
Q: What foundational AI and deep learning concepts will I learn in this course?
You’ll dive into the core principles of neural networks and understand how they mimic brain functions to solve complex problems. Key topics include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Self-Organizing Maps (SOM), Boltzmann Machines, and AutoEncoders.
Q: How does the course help me apply deep learning to real-world problems?
You’ll work with practical datasets to predict customer churn, classify images, forecast stock prices, detect fraud, and build recommendation systems—equipping you to solve challenges across various industries using powerful deep learning models.
Q: What skills will I develop in building and tuning deep learning models?
You’ll learn to construct, train, and evaluate neural networks using Python. Skills include data preprocessing, backpropagation, gradient descent, model optimization, and hyperparameter tuning to improve accuracy and performance.
Q: Will I learn how to handle different types of data in deep learning tasks?
Yes. The course covers working with tabular data, image data, time-series data, and recommendation data, giving you hands-on experience in adapting neural networks to a variety of data structures.
Q: How does this course prepare me for applying deep learning in a professional context?
You’ll gain the technical knowledge and intuition to integrate deep learning into business applications, enhance decision-making, automate tasks, and drive innovation in fields such as finance, healthcare, e-commerce, and more.
Submit your interest today !
