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Reading list

What is Deep Learning? DL vs ML vs AI Why Deep Learning is So Popular?
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Gradient Descent Algorithm Variants of Gradient Descent Algorithm
Introduction to Loss Function Binary and Categorical Cross Entropy
Why Do We Need Activation Functions? Linear Activation Function Sigmoid and Tanh Function Softmax How to Select Right Activation Function?
Perceptron Weights in Perceptron Introduction to Artificial Neural Network
Understanding Forward Propagation Mathematically Understand Backward Propagation Mathematically Implementing Neural Networks in Python
Problems with Gradient Descent Gradient Descent with Momentum Adagrad and Adadelta
Introduction to Learning Rate Schedulers
Overview of Deep Learning Frameworks Implementing Neural Networks using Keras Functional API in Keras Implementing Neural Networks using Keras Hyperparameter Tuning of MLP in Keras
Understanding Early stopping Understanding Dropout Vanishing and Exploding Gradients Weights Initialization Techniques Implementing Weight Initializing Techniques Batch Normalization Image Augmentation Techniques Image Generator and Fit Generator Model Checkpointing Implementing Model Checkpointing Dealing with Class Imbalance Ensemble Deep Learning
Introduction to GPU and TPU TensorRT
Introduction to Unsupervised Learning How to Solve Unsupervised Learning Problems? Introduction to Autoencoders Implementing Autoencoders
A Beginners Guide to Codeless Deep Learning
TensorFlow Serving Build Deep Learning Models for Android
Introduction to PyTorch and Tensors Mathematical and Matrix Operations in PyTorch Important PyTorch Modules Implement CNN in PyTorch Transfer Learning in PyTorch Working with Text Data in PyTorch Building a RNN model in PyTorch Autoencoders in PyTorch

Infographic – Learning Plan 2017 for Intermediates in data science

Kunal Jain
Kunal Jain Last Updated : 02 Feb, 2017
< 1 min read

Introduction

We believe, learning should never stop. This plan is for people with basic knowledge of machine learning or deep learning. You can advance your learning this year using this plan.

Depending on your skills and learning agenda for the year, you can choose the area you to learn. The plan starts with various skill assessment and makes sure that you are on top of data science domain by end of the year. The plan contains both theoretical as well practical examples. We have also provided you with resources / tests to apply your learning and benchmark yourself.

This learning plan should provide a structured path to anyone who wants to learn advanced concepts of machine learning, deep learning and data science. To download the learning plan click here.

Access the complete plan here

Learn, compete, hack and get hired!

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Srinath
Srinath

Hi Kunal, I am currently trying to make a transition into data analytics. I have been advised to start off with Business Analytics involving SAS, R, Excel, Tableau, etc... along with Statistics with basic machine learning and I intend to eventually include Python, Advanced Machine Learning, Big Data, Hadoop, etc... and make the transition to a data scientist. Kindly advise if I have the right approach. Business Analytics -- > Data Scientist

123

Chetan
Chetan

Hey! Thanks a lot for the useful information. Really helps!! I wanted to ask : is there a specific reason for specifying flask framework particularly ?

123

hammad zahid
hammad zahid

nice guide. thank you very much

123

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