Deep Learning & AI Course
Sekh Abdul Hannan
Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.
Deep learning minimizes the need for human action since its algorithms conduct feature extraction on their own. This makes the process much faster and reduces the risk of human error.
This course gives and understanding of the theoretical basis underlying neural networks and deep learning. Furthermore, the course includes implementation of neural components and as well as applying deep learning on real-world data sets using modern deep learning packages.
“When engaged in deeper learning, students think critically and communicate and work with others effectively across all subjects. Students learn to self-direct their own education and to adopt what is known as 'academic mindsets' and they learn to be lifelong learners.”
Get Started with Deep Learning
To get started with a career in Deep Learning, an individual is expected to possess the following skills:
- Basic understanding of a programming language like Python/R/Scala.
- Since most deep learning concepts are mathematically rigorous, you must have a strong foundation in advanced mathematical concepts.
- In-depth knowledge of building and deploying machine learning projects.
- Ability to convert a data science problem into a deep learning problem.
- Good communication skills to convey the results of a deep learning based solution.
- Importance and Applications of Programming Languages
- Intro to Python
- Intro to Jupyter Notebook
- Variables and Data-types in Python
- Operators in Python
- Tokens in Python
- Strings in Python
- Data Structures in Python
- If Statement in Python
- Looping Statements in Python
- Functions in Python
- Intro to Object Oriented Programming in Python
- Creating the First Class in Python
- Adding Parameters to a Class Method
- Creating a Class with Constructor
- Inheritance in Python
Phrase 1: Python Fundamentals for Beginners
- Intro to Numpy
- Joining NumPy Arrays
- Numpy Intersection & Difference
- Numpy Array Mathematics
- Saving and Loading Numpy Array
- Intro to Pandas
- Pandas Series Object
- Intro to Pandas Dataframe
- Pandas Functions
Phrase 2: Python for Machine Learning
- What is Deep Learning?
- Where DL Fits and Where to Use DL?
- Brief History
- Why second wave?
- ML vs. DL
- Artificial Neural Network Introduction
- Tensorflow Playground Demo
- Deep Learning Fundamentals
- Basic Set of Layers
- Activation Function
- Demo for Neural Network
- CNN Introduction
- RNN & LSTM
- Types of Chatbots & Conventional Interfaces
- Demo for CNN
- Deep Neural Network Overview
- Introduction to Deep Neural Networks
- Boolean Gate and Artificial Neuron
- Rosenblatt Neuron Perceptron
- Artificial Neural Network
Phrase 3: Introduction to Deep Learning
- Introduction for tensorflow
- Brief About TensorFlow-2
- What are Tensors?
- How to install TensorFlow?
- Introduction to neural networks
- Getting Started with TensorFlow
- Basic Demo on TensorFlow-2
- Linear Regression Using TensorFlow-2
- Demo #1: MNIST Character Recognition with TensorFlow
- Demo #2: Binary classifier using Convolutional Neural Network
- Getting Started with Keras-2
Phrase 4: Introduction to Tensorflow and Keras
- Class Start: November 10, 2023
- Course Duration: 6 Months
- Total Credits: 25
- Student Capacity: Max 40 Students
- Class Schedule: Saturday
- Class Time: 11.00 am - 02.00 pm 03.00 pm - 06.00 pm
- Course Teachers: 01