You’re interested in deep learning, computer vision, neural networks..but you don’t know where to get started. You’ve come to the right deep learning course!
Deep learning algorithms are revolutionizing the industry, from computer vision applications to natural language processing (NLP) use cases. 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 are looking to hone your existing deep learning and neural network skills, you will 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?
What kind of computer vision and NLP projects can you solve using deep learning?
This comprehensive course on deep learning using Python will guide you on everything you need to know about deep learning. We will start from scratch - introduction to deep learning and neural networks, discuss concepts like forward propagation, backpropagation, loss functions - and amalgamate your learning with real-world deep learning projects!
Deep learning algorithms open up a whole new world of possibilities! Here’s just the top of the funnel of what you can do using deep learning and Python:
Computer Vision Tasks using Deep Learning
- Image classification
- Object detection
- Face recognition
- Image segmentation
- Object tracking, among other things
Natural Language Processing (NLP) Tasks using Deep Learning
- Text classification
- Text summarization
- Text generation, and much more!
Who is the ‘Fundamentals of Deep Learning’ course for?
The Fundamentals of Deep Learning course is meant for anyone interested in the deep learning field. Whether you want to understand:
- How deep learning works
- What are the different neural networks in deep learning
- How to design the different neural networks including CNN and RNN, or
- How the overall deep learning pipeline works
Then this course is for you!
Prerequisites for the ‘Fundamentals of Deep Learning’ course
So, what do we expect from you?
- Python - You should know how to work with Python programming
- Basics of machine learning would be beneficial. But don’t worry! We have provided the required learning resources in the course itself
- A whole lot of deep learning enthusiasm!
- A working laptop / desktop with 8 GB RAM
- A working Internet connection
- Basic knowledge of Machine Learning
- Basic knowledge of Python - check out this Course first, if you are new to Python
This is all it takes for you to learn Deep Learning.
What are you waiting for?
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.
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. 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 Non-Emergency)
You will solve emergency vs non-emergency 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, pre-trained models, how you should select a pre-trained model depending on the problem at hand. You will also use transfer learning to solve “Emergency vs Non-emergency” 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 pre-processing 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
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.
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 is a Data Scientist at Analytics Vidhya. He has a multidisciplinary academic background and rich experience in BFSI and E-Learning 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 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.
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 8-10 hours per week to complete the “Fundamentals of Deep Learning” course and apply your learning on real-world 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 real-world 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 can be availed through any of the following channels:
- Phone - 10 AM - 6 PM (IST) on Weekdays Monday - Friday on +91-8368253068
- Email email@example.com (revert in 1 working day)
- Live interactive chat sessions (https://support.analyticsvidhya.com/ ), Monday to Friday between 7 PM to 8 PM IST.