Learning Path for Deep Learning
2019, Jan 22
If there is one area in data science which has led to the growth of Machine Learning and Artificial Intelligence in the last few years, it is Deep Learning. From research labs in universities with low success in industry to powering every smart device on the planet – Deep Learning and Neural Networks have started a revolution.
Expected Effort: 1012 hours per week
Deep learning is ubiquitous – whether it’s Computer Vision applications or breakthroughs in the field of Natural Language Processing, we are living in a deep learningfueled world.

January: Getting Started With Deep Learning and Refresher to Python/ Statistics
 Overview of the learning path
 Getting started with Deep Learning
 Setting up the system
 Descriptive Statistics & Probability
 Getting Started With Python

February: Understanding Inferential Statistics & Basics Of Machine Learning
 Inferential Statistics
 Partial Derivative
 Linear Algebra  Part 1 (Intro To Matrix & Vectors)
 Linear Regression
 Logistic Regression
 Regularization Techniques (Ridge & Lasso)
 Project on Classification
Start engaging in Data Science/Deep Learning Communities

March: Introduction To Deep Learning and Keras
 Linear Algebra  Part 2 (Matrix Multiplication & Inverse Of a matrix)
 Understanding Neural Networks (MLP)
 Build your first Neural Network in Numpy
 Frameworks For Deep Learning
 Intro To Keras
 Build Neural Network in Keras
 Project on Classification Using Deep Learning
Start Building Your Github Profile

April: Improving Your Neural Network
 Handling/ PreProcessing Images
 Regularization Techniques
 Hyperparameter Tuning
 Transfer Learning
 Data Augmentation
 Project 1 on Computer Vision

May: Understanding Convolutional Neural Network
 Understanding CNN
 Hyperparameter Tuning
 Project 2 on Computer Vision
Participate In Competition

June : Debugging Your Deep Learning Model
 Visualizing Deep Learning Model
 Project 3 on Computer Vision
 Project 4 on Computer Vision

July : Sequence Models
 Sequence Models (RNN, LSTM, GRU)
 Handling/ PreProcessing Text Data

Project 1 on NLP
 Write Blogs

August : Word Embeddings
 Word Embeddings
 Project 2 on NLP

September : Attention Models
 Attention Models
 Project On MultiModel Task

October: Unsupervised Deep Learning
 Unsupervised Deep Learning (Autoencoders)
 Project On Unsupervised Deep Learning

November: GANS
 GANS
 Projects On GANS

December: Way Forward
 Up Level your Data Science Resume Course
 Ace Data Science Interview Course