About Deep Learning Basics (DLSP)

Deep learning algorithms are revolutionizing data science industry and disrupting several domains. From computer vision applications to natural language processing (NLP) use cases - every field is benefitting from use of Deep Learning models.

This course starts by assuming no knowledge about Neural Networks and Deep Learning and introduces these subjects to the student one by one.

The course helps you build a deep as well as intuitive understanding of what is Deep Learning, where can Deep Learning Models be applied and then helps you solve several real life problems using Keras.

Key takeaways from Deep Learning Basics

Introduction to Deep Learning

This module will introduce you to the world of Deep Learning. You will learn various applications and use cases of deep learning in the real world.

 

Introduction to Perceptron and Multi Layer Perceptron (Artificial Neural Network)

Then you will understand what multi layer perceptron, how they work and you will also visualize a neural network using TensorFlow playground.

 

Understanding Forward and Backward propagation (Along with its maths)        

We will dive in mathematics behind forward and backward propagation. We will see how gradient descent is helping to update weights and biases of neural networks.

 

Implementing Neural Network from scratch (NumPy)

You will implement your first neural network from scratch using NumPy. You will code all the concepts of forward and backward propagation in Python.

 

Activation functions

Discuss why we need activation functions , several types of activation functions, and when should we use which one?

 

Type of Optimizers

We will discuss several optimizers like Momentum, RMSProp, Adam. You will also learn how you can implement these optimizers from scratch.

 

Loss Functions for Neural Network

In this module, you will learn several types of loss functions like Mean-Squared-Error, Binary-Cross-Entropy, Categorical- Cross-Entropy and others. We will also discuss use cases of these loss functions in different scenarios.

 

Introduction to Keras

You will get an overview of different deep learning frameworks like PyTorch, TensorFlow, and Keras. We will discuss various steps of model building using Keras.

 

Project: Solving Loan Prediction problem using Neural Network in Keras

You will build your first deep learning model on structured dataset. We will solve the “Loan Prediction problem” using neural networks in Keras.

 

Getting started with Image Data

This module will teach you the basics about images, how you can deal with them. You will learn how to read single as well as multiple images and then perform pre-processing on them like cropping, rotating, detecting edges, etc.

 

Project: Solving Image Classification problem using Neural Network (Emergency vs Non-Emergency)

You will solve an image classification problem of classifying images of vehicles as emergency or non-emergency using neural networks. You will implement this project in Keras. 


Improving your Deep Learning Model

In this module, you will learn different techniques that can be used to improve your deep learning model. We will cover techniques like Early Stopping, Dropout, Regularization, Batch Normalization, Image Augmentation etc.

Course curriculum

  • 1
    Introduction to Deep Learning
  • 2
    Getting ready for the course
  • 3
    Introduction to the course
  • 4
    Introduction to Neural Network
  • 5
    Forward and Backward Propagation
    • Understanding Forward Propagation Mathematically
    • Quiz - Understanding Forward Propagation Mathematically
    • Understanding Backward Propagation Mathematically
    • Quiz - Understanding Backward Propagation Mathematically
    • Backward Propagation: Matrix Form
    • Why Numpy?
    • Neural Network From scratch Using Numpy
    • Forward Propagation (using Numpy)
    • Backward Propagation (using Numpy)
    • Training network (using Numpy)
  • 6
    Activation Functions
    • Why do we need activation functions?
    • Quiz - Why do need activation functions
    • Linear Activation Function
    • Quiz - Linear Activation Function
    • Sigmoid and tanh
    • Quiz - Sigmoid and tanh
    • ReLU and Leaky ReLU
    • Quiz - ReLU and LeakyReLU
    • Softmax
    • Quiz - Softmax
    • Tips to selecting right Activation Function
  • 7
    Optimizers
    • Variants of Gradient Descent
    • Quiz - Variants of Gradient Descent
    • Problems with Gradient Descent
    • Quiz - Problems with Gradient Descent
    • RMSProp
    • Quiz - RMSPro
    • Adam
  • 8
    Loss Function
    • Introduction to loss function
    • Quiz - Introduction to Loss Function
    • Binary and Categorical Cross entropy / log loss
    • Quiz - Binary and Categorical cross entropy / log loss
  • 9
    NN on structured Data
    • Overview of Deep Learning Frameworks
    • Quiz - Overview of deep learning frameworks
    • Understanding the problem statement: Loan Prediction
    • Data Preprocessing: Loan Prediction
    • Quiz - Data Preprocessing: Loan Prediction
    • Steps to solve Loan Prediction Challenge
    • Loading loan prediction dataset
    • Defining the Model Architecture for loan prediction problem
    • Training and Evaluating model on Loan Prediction Challenge
    • Quiz - Training and Evaluating model on Loan Prediction Challenge
  • 10
    Assignment: Big Mart Sales Prediction
    • Assignment: Big Mart Sales Prediction
  • 11
    Functional API in Keras for Deep Learning
    • Functional API for Deep Learning
    • Quiz - Functional API for Deep Learning
    • Solving Loan Prediction Using Functional API in Keras
    • Building a custom Model Using Functional API in keras
    • Quiz - Building a Model Using Functional API
  • 12
    Getting started with image data
    • How are images stored?
    • Quiz - How are images stored
    • Different Image Formats
    • Quiz - Different Image formats
    • Reading and stacking Images
    • Converting images into different formats
    • Quiz - Converting image into different formats
    • Extracting edges from images
    • Quiz - Extracting edges from images
    • Implementation: Extracting edges from images
    • Quiz - Extracting edges implementation
  • 13
    Solving Image Classification Using Keras
    • Project: Image Classification "Emergency Vs Non-Emergency Vehicle"
    • Notebook: Neural Network in Keras and Hyperparameter Tuning
    • Neural Network in Keras
    • Hyperparameter Tuning for MLP in Keras
  • 14
    Assignment: Gender Classification
    • Assignment: Gender Classification
  • 15
    Improving your Deep Learning Model
    • Early stopping
    • Early stopping: Implementation
    • Quiz - Early stopping: Implementation
    • Dropout
    • Dropout: Implementation
    • Quiz - Dropout: Implementation
    • Vanishing and Exploding gradients
    • Vanishing and Exploding gradients: Implementation
    • Quiz - Vanishing and exploding gradient: Implementation
    • BatchNorm
    • BatchNorm: Implementation
    • Advantages of Batch Normalization
    • Quiz - Advantages of Batch Normalization
    • Image Augmentation on Emergency-non emergency dataset
    • Image Augmentation Techniques
    • Image Augmentation Techniques: Implementation
    • Image Generator and Fit Generator
    • Assignment: Gender Classification
  • 16
    What's Next
    • Where to go from here?

Project-1:Loan Eligibility Prediction (In-class)

Automate the loan eligibility process (real time) based on customer detail provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, Identify those who are eligible for loan amount so that they can specifically target these customers using feed forward neural networks.

Project-2: Classify Emergency Vehicles from Non-Emergency Vehicles

Fatalities due to traffic delays of emergency vehicles such as ambulance & fire brigade is a huge problem. In daily life, we often see that emergency vehicles face difficulty in passing through traffic. So differentiating a vehicle into an emergency and non emergency category can be an important component in traffic monitoring as well as self drive car systems as reaching on time to their destination is critical for these services. In this project, you will get to design a computer vision system that can do just this.

Instructor(s)

  • Aishwarya Singh

    Aishwarya Singh

    Aishwarya is currently working as a Data Scientist at Analytics Vidhya. She is one of the primary content curators and an instructor for Analytics Vidhya’s most popular course – Applied Machine Learning. She is also an avid reader and blogger who loves exploring the endless world of data science and artificial intelligence. She has written over 70 articles in recent years on various machine learning and deep learning topics and applications.
  • Kunal Jain

    Founder & CEO

    Kunal Jain

    Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
  • Pulkit Sharma

    Pulkit Sharma

    Pulkit is a Data Scientist at Analytics Vidhya. His research area lies in the field of Computer Vision and Deep Learning. He has been working on various projects related to images and videos for the past few years. He is comfortable with Python, Keras, PyTorch and has done multiple projects using these frameworks and tools. Some of his key projects include Crowd Counting, Estimating the Screen Time in videos, Object Detection, and Image Segmentation. He is one of the primary content curators for Analytics Vidhya’s courses, such as the Computer Vision using Deep Learning and Applied Machine Learning. He is also an avid blogger and has written multiple detailed and in-depth guides on various computer vision topics and applications, ranging from Image Classification to Object Detection and Image Segmentation.

FAQ

  • Do I need to install any software before starting the course ?

    You will get information about all installations as part of the course.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • How long I can access the course?

    You will be able to access the course material for next 7 days.

Support for Deep Learning Basics Course

Support for Deep Learning Basics course can be availed through any of the following channels:

  • Email [email protected] (revert in 1 working day)

  • Discussion Forum - answer in 1 working day