Your Journey to Master Deep Learning Starts Here
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 and PyTorch frameworks.
Tools and Techniques covered in Fundamentals of Deep Learning Course
Fundamentals of Deep Learning covers every tool a data scientist needs to build Deep Learning models

Convolution Neural Networks (CNN)

Recurrent Neural Networks (RNNs)

Advanced Sequence Models including LSTM & GRU

Transfer Learning
Tools Covered in the Fundamentals of Deep Learning Course
Key takeaways from Fundamentals of Deep Learning Course
These deep learning algorithms are powered by techniques like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), etc.
If you’re a beginner in deep learning, or you are looking to hone your existing deep learning and neural network skills, you must have asked these questions:
What is deep learning?
What are the different types of neural networks?
What part do these neural networks play in the deep learning space?
When should you choose convolutional neural networks (CNN) over recurrent neural networks (RNN)  and vice versa?
How can you build deep learning models in Python?
What is forward propagation? And what is backpropagation?
What are the different loss functions in deep learning?
What are the different deep learning frameworks?
TensorFlow vs. PyTorch vs. Keras  which deep learning framework should you choose?
The course is designed to answer all the above questions in an applied first methodology.
Course curriculum

2
Getting ready for the course
 Hardware for Computations in Deep Learning FREE PREVIEW
 Setting up your system
 Introduction to Google Colab FREE PREVIEW
 Understanding Google Colab Interface FREE PREVIEW
 Prerequisites for Deep Learning

4
Introduction to Neural Network
 Perceptron FREE PREVIEW
 Quiz  Perceptron
 Weights in Perceptron FREE PREVIEW
 Quiz  Weights in Perceptron
 Multi Layer Perceptron FREE PREVIEW
 Quiz  Multi Layer Perceptron
 Visualizing the neural network FREE PREVIEW
 Understanding Decision Boundary
 Quiz  Visualizing the neural network
 Forward and Backward Prop Intuition
 Quiz  Forward and Backward Prop Intuition
 Gradient Descent Algorithm
 Quiz  Gradient Descent Algorithm

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 important Kears modules
 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 NonEmergency 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
 Weights Initialization Techniques
 Implementing different weight initializing techniques
 BatchNorm
 BatchNorm: Implementation
 Advantages of Batch Normalization
 Quiz  Advantages of Batch Normalization
 Image Augmentation on Emergencynon emergency dataset
 Image Augmentation Techniques
 Image Augmentation Techniques: Implementation
 Image Generator and Fit Generator
 Assignment: Gender Classification
 Model Checkpointing
 Implementing model checkpointing

16
Introduction to Convolutional Neural Network and Implementation
 Why do we need CNN?
 Quiz  Why do we Need CNN
 How filters work?
 Quiz  How filters work?
 Filters in CNN
 Quiz  Filters in CNN
 Parameter Sharing and Local Connectivity in CNN
 Quiz  Parameter Sharing and Local Connectivity
 CNN Architecture
 Quiz  CNN Architecture
 Pooling
 Quiz  Pooling
 CNN Forward Propagation
 Quiz  CNN Forward Propagation
 CNN Backward Propagation
 Quiz  CNN Backward propagation
 CNN Backprop : Matrix Form (Optional)
 Convolutional Neural Network in Keras
 Quiz  CNN in keras
 Hyperparameter Tuning for CNN in keras
 Quiz  Hyperparameter Tuning for CNN in keras
 Assignment: Gender Classification
 Model Checkpointing in CNN

17
Introduction to Transfer Learning
 Introduction to Transfer Learning FREE PREVIEW
 How to select right pretrained model?
 Exercise : How to select right pretrained model?
 Steps to solve emergency vs non emergency vehicle classification challenge using transfer learning
 Exercise : Steps to build the model using transfer learning
 Solving the challenge using transfer learning (Notebook)
 Solving the challenge using Transfer Learning (Part I)
 Exercise: Solving the challenge using Transfer Learning
 Solving the challenge using Transfer Learning (Part II)
 Exercise: Solving the challenge using Transfer Learning II
 Solving the challenge using Transfer Learning (Part III)
 Exercise : Solving the challenge using transfer learning  III
 Different fine tuning techniques
 Transfer Learning: Using the architecture of the pretrained model
 Transfer Learning: Freezing some layers and training others
 Advantages of transfer learning
 Assignment: Gender Classification

18
CNN Visualization
 Introduction to Neural Network Visualization
 Quiz  Introduction to Neural Network Visualization
 How can we Interpret a Neural Network?
 Setting up the System
 Attempt 1: Understand the model architecture
 Quiz  Attempt 1: Understand the model architecture
 Attempt 2: Visualize the filters / weights
 Quiz  Attempt 2: Visualize the filters / weights
 Attempt 3: Extract the output of intermediate layers
 Quiz  Attempt 3: Extract the output of intermediate layers
 Attempt 4: Locate important parts of the image
 Quiz  Attempt 4: Locate important parts of the image

19
Real World Use Cases of Deep Learning
 Object Detection, segmentation, image generation
 Quiz  Object Detection, Segmentation, image generation
 Sequential Modeling
 Quiz  Sequential Modeling

20
Working with Text Data
 Getting Started with Text Data
 Quiz  Getting Started with Text Data
 Introduction to Text Preprocessing
 Quiz  Intro to TextPreprocessing
 Regular Expressions in Action  I
 Regular Expressions in Action  II
 Regular Expressions in Action  III
 Quiz  Regular Expressions in Action
 Understanding Text Representation
 Introduction to OneHotEncoding
 Quiz  Introduction to OneHotEncoding
 Implementation: OneHotEncoding
 Limitations of OneHotEncoded Vectors
 Quiz  Limitations of OneHotEncoded Vectors
 Word Embeddings
 Implementation: Word Embeddings
 Quiz  Word Embeddings

21
Getting Started with Recurrent Neural Networks (RNN)
 Journey So Far
 Introduction to RNN
 Quiz  Introduction to RNN
 Forward Propagation in RNN
 Quiz  Forward Propagation in RNN
 Backward Propagation Through Time  Part 1
 Backward Propagation Through Time  Part 2
 Backward Propagation Through Time  Part 3
 Quiz  BPTT

22
Project  Building an AutoTagging System
 Overview of AutoTagging System
 Quiz  Overview of AutoTagging System
 Understanding the Dataset
 Merging Different Datasets
 Dataset Preparation
 Model Building and Tags Prediction
 Quiz  Model Building and Tags Prediction

23
Advanced Sequence Models  LSTM & GRU
 Shortcomings of RNN
 Quiz  Shortcomings of RNN
 What is Long Short Term Memory (LSTM) Network?
 Quiz  What is Long Short Term Memory (LSTM) Network?
 What is Gated Recurrent Unit (GRU) Network?
 Quiz  What is Gated Recurrent Unit (GRU) Network?
 Solving AutoTagging Problem using LSTM and GRU
 How to use CNN for Text Data?
 Quiz  CNN for Text Data
 Solving AutoTagging Problem using CNN

24
Assignment: Identify the Sentiments
 Assignment: Identify the Sentiments

25
Project  Web Traffic Forecasting
 Overview of Web Traffic Forecasting Problem
 Quiz  Overview of Web Traffic Forecasting Problem
 Data Exploration and Preprocessing
 Quiz  Data Exploration and Preprocessing
 Model Building and Forecasting

26
Project  Emergency vs Non Emergency Vehicle Sound Classification
 Introduction to Audio Data
 Understanding the Audio Classification Problem
 Audio Data Preparation
 Audio Classification using Time Domain Features
 Audio Classification using Spectrogram features

27
Assignment: Urban Sound Classification
 Assignment: Urban Sound Classification

28
Unsupervised Deep Learning
 Recap of Previous Modules
 Introduction to Unsupervised Learning
 Quiz  Introduction to Unsupervised Learning
 How to Solve Unsupervised Learning Problems?
 Introduction to Autoencoders
 Quiz  Introduction to Autoencoders
 Photo Gallery Organizing using Autoencoders
 Dataset: Photo Gallery Organization
 Implementation: Photo Gallery Organization
 Unsupervised Deep Learning  Problems and Research

29
Assignment: Image Denoising
 Assignment: Image Denoising
 Image Denoising Dataset

30
Introduction to PyTorch
 Overview of the Module
 Introduction to PyTorch and tensors
 Mathematical and matrix operations in PyTorch  Part I
 Mathematical and matrix operations in PyTorch  Part II
 Mathematical and matrix operations in PyTorch  Part III
 Neural Network from scratch in PyTorch  Part I
 Neural Network from scratch in PyTorch  Part II
 Neural Network from scratch in PyTorch  Part III
 Important PyTorch modules (Autograd, nn, optim, etc.)
 Solving image classification problem using MLP in PyTorch  Part I
 Solving image classification problem using MLP in PyTorch  Part II
 Solving image classification problem using MLP in PyTorch  Part III
 Solving image classification problem using MLP in PyTorch  Part IV
 Convolutional Neural Network in PyTorch
 Hyperparameter Tuning of CNN
 CNN Improvements
 Transfer Learning in PyTorch  Part I
 Transfer Learning in PyTorch  Part II
 Working with text data in pytorch
 Text preprocessing in pytorch
 Building an RNN model in PyTorch
 Evaluating RNN model
 Building and evaluating LSTM in PyTorch
 Autoencoders in PyTorch

31
What's Next?
 Where to go from here?
Project 1
Loan Eligibility Prediction (Inclass)
Project 2
Classify Emergency Vehicles from NonEmergency Vehicles (Inclass)
Project 3
AutoTagging StackOverflow Queries (Inclass)
Project 4
Web Traffic Forecasting (InClass)
Project 5
Project5: Audio Classification (Inclass)
Certificate of Completion
Instructor(s)

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. 
Prateek Joshi
Prateek is a Data Scientist at Analytics Vidhya. He has a multidisciplinary academic background and rich experience in BFSI and ELearning industries. Prateek's strengths include expertise in Natural Language Processing (NLP) and Machine Learning. He is well versed in Python, R and most of the libraries and frameworks around machine learning and NLP. He has taken various trainings around NLP and Data Science and he is also a course instructor and content creator at Analytics Vidhya. 
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 indepth guides on various computer vision topics and applications, ranging from Image Classification to Object Detection and Image Segmentation.
FAQ
Common Questions Answered about the Fundamentals of Deep Learning course

Who should take the Fundamentals of Deep Learning course?
This course is for beginners in deep learning. You don’t need to know anything about neural networks  we’ll cover all of that inside the course!

How long would I have access to the “Fundamentals of Deep Learning” course?
Once you register, you will have 6 months to complete the course.

How much effort will this course take?
We suggest spending 810 hours per week to complete the “Fundamentals of Deep Learning” course and apply your learning on realworld projects. The time taken on projects varies from person to person.

What kind of deep learning projects can I take up?
We have provided plenty of realworld deep learning projects in the course, including object detection, image classification, among others.

What is the fee for this course?
The fee for this course is Rs. 7,999.

Can I download videos from this course?
We regularly update the "Fundamentals of Deep Learning" course and hence do not allow for videos to be downloaded.

Which programming language is used in this course?
Deep learning using Python  that’s the idea behind this course. Python is the most popular language for deep learning and you’ll see why when you take the course!
Support for Fundamentals of Deep Learning Course
Support for Fundamentals of Deep Learning course can be availed through any of the following channels:

Phone  10 AM  6 PM (IST) on Weekdays (Mon  Fri) on +918368253068

Email training_support@analyticsvidhya.com (revert in 1 working day)

Live interactive chat sessions (https://support.analyticsvidhya.com/ ), Monday to Friday between 7 PM to 8 PM IST.

Discussion Forum  answer in 1 working day
Course Curriculum in Detail
Fundamentals of Deep Learning
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 MeanSquaredError, BinaryCrossEntropy, Categorical CrossEntropy 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 preprocessing on them like cropping, rotating, detecting edges, etc.
Project: Solving Image Classification problem using Neural Network (Emergency vs NonEmergency)
You will solve an image classification problem of classifying images of vehicles as emergency or nonemergency using neural networks. You will implement this project in Keras. You will also learn how to tune hyperparameters of a neural network.
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.
Introduction to Convolutional Neural Networks (CNNs)
In this module, you will understand one of the most famous deep learning architecture convolutional neural networks (CNNs) for image data. You will learn how they works, different components of CNN and architecture
Project: Solving Image Classification problem using CNN (Emergency vs NonEmergency)
You will solve emergency vs nonemergency vehicle classification problems using CNN and compare results with the previous deep learning model (MLP). You will also learn different hyperparameter tuning techniques for CNN.
Introduction to Transfer Learning
In this module, you will learn what is transfer learning, pretrained models, how you should select a pretrained model depending on the problem at hand. You will also use transfer learning to solve “Emergency vs Nonemergency” vehicle classification problem.
Visualizing Neural Networks
We will discuss several techniques to visualize your deep learning model and try to interpret reasons behind model performance.
Horizon of Deep Learning
We will explore different use cases of deep learning other than image classification like object detection, Image Segmentation, Image Captioning, Time series, text classification, audio processing and video data.
Working with Text Data
In this module the focus will be handling text data. You will learn essential text preprocessing techniques and vector representation of text.
Getting started with Recurrent Neural Network (RNN)
RNN is a special neural network architecture that is used to process sequence based information. We will also discuss forward and backward propagation methods of RNN.
Advance Sequence Models: LSTM and GRU
There are certain drawbacks of RNN which are addressed by an advanced sequence modeling architectures such as LSTM and GRU. You will learn about LSTM and GRU architectures and they function.
Project: Automatic Tagging System for Text Data
In this project, we will apply the learnings of Sequence Modeling to build a automatic tagging model for text data.
Project: Time Series Forecasting
In this project, we will learn to apply deep learning sequence models(LSTM) on time series data and do forecasting.
Project: Audio Classification
After working on image and text data, in this project, you will work with audio data. You will also learn to create features from raw audio and then use the features to build an audio classification model
Unsupervised Deep Learning
Supervised Learning is what is covered in the previous modules. Unsupervised learning algorithms derive insights directly from the data itself without labels, and work as summarizing the data or grouping it, so that we can use these insights to make data driven decisions. Here, we will understand Unsupervised Deep Learning and showcase its use for image categorization without using labels.
Introduction to PyTorch
Till now we have worked with Keras to solve all the projects of image classification, text classification, and others. Keras will solve almost 95% of your projects but when we move to advanced projects like object detection, image segmentation, it gets difficult to solve them using keras. Here, we will introduce you to another deep learning framework PyTorch and installation steps of PyTorch.
Build Deep Learning Models using PyTorch
In this module, we will build MLP, CNN and RNN models using PyTorch for various challenges like Image classification, Text Classification, Time Series and audio classification.