Deep learning for everyone

Free python course bundled with this

You need to know basic python which you can learn through our short recorded course free with this course

Everything is discussed from scratch

Let it be matrices, dot product, calculus and code, everything is discussed with basics and patience

No mathematics required

All the mathematical prerequisites are included in this course so you don't have to worry about your background

Recorded sessions

Live sessions are recorded are available on demand to keep for lifetime so that you can learn anytime anywhere

Book your seat for the demo class

Seats for Batch 2 are full % Batch 3 registrations are not started yet

Frequently asked questions

You will be added to a WhatsApp group where we will share you a invite link to class everyday also you can discuss topics with your fellow students there

The course fee is INR 4,999 which includes:

  1. 30 Classes of training
  2. Free python course bundled with this
  3. Lifetime access to session recordings 
  4. Eligibility for 20% discount to our Retro Machine Learning course 

Yes there is a prerequisite of Python programming and we are providing you our python course free with this course and python programming basics will also be discussed in live classes as well

This course is specially designed to teach deep learning to anyone in 30 classes with basic python programming as prerequisite. After this course you will be able to solve all the real world problems using deep learning as we teach you all the mathematics so you can innovate with algorithms

Ideal student for the course

Mental Requirements
Somebody who is willing to be dedicated during the course duration, can watch videos and grasp the knowledge as the content is made super easy for everyone. Someone who can watch videos and do assignments without delay with discipline.
Physical Requirements
Someone who is looking to build a career in  Deep Learning Engineer, Computer Vision, NLP, Robotics can join this course as we cover from the very basics to very advance.
All you need is a goal of what you want to achieve after this course and dedication if both meets, enroll yourself

Course Curriculum

  1. Intro to AI, ML, DS and all the course curriculum, future aspects
  2. Intro to Probability, Statistics, Application of Programming in ML/DS/AI
  3. Explanation of syntax and things in Python
  4. Introduction to Deep learning: What is a Neural Network?
    1. Understanding the math behind neural network
    2. Weights and Bias
    3. Forward Propagation and Derivatives (Forward Pass)
    4. Backward Propagation and Derivatives (Backward Pass)
    5. Activation Functions
    6. Coding vanilla neural network from scratch in python
    7. Tensorflow deep detailed session
    8. Making ANN to do housing prices prediction using Tensorflow
    9. Making ANN to do MNIST digit recognition(60k Images) using Tensorflow
  5. Introduction to Computer Vision & Image processing
    1. Introduction to Images in terms of matrices
    2. Introduction to Filters and Convolution(Non Mathematical manner more into intuition)
    3. Implementing Convolution on image and applying different filters
  6. Convolutional Neural NetworkArchitecture:Padding
    1. Strided Convolutions
    2. One Convolutional Layer
    3. Pooling Layers
    4. Functions in Tensorflow for CNN
    5. Coding CNN for Cat & Dog image recognition using core Tensorflow
  7. Autoencoders & Variational Autoencoders
    1. Use casesAnomaly Detection
    2. Image Denoising
    3. Data Compression
    4. Architecture
    5. Introduction to Keras(Derivative of Tensorflow)
    6. Coding in Tensorflow as well as Keras
  8. Generative Adversarial Networks: Very fascinating. You can generate your own fake images which have been clicked yet. New Fake images/videos generation
  9. Deep Learning for Time Series Data, Audio Processing and Natural Language Processing:
    1. Sequence Models
    2. Recurrent Neural Network Model
    3. Different types of Recurrent Neural Networks
    4. Language model and sequence generation
    5. Gated Recurrent Unit (GRU)
    6. Long Short Term Memory (LSTM)
    7. Deep Recurrent Neural Networks
    8. Word Representation
    9. Word embeddings Word2Vec
    10. GloVe word vectors