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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

Deep Learning Session with Microsoft at SP Jain School of High Technology, Mumbai, 5 Dec 2016

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Analytics Vidhya Last Updated : 03 Dec, 2016
< 1 min read

Introduction

SP Jain School of High Technology is organizing  a power-packed session on Deep Learning with Microsoft’s Open Source Cognitive Toolkit (CNTK). The session will be conducted personally by none other than Mr. Sayan Pathak, the Global Principal Program Manager Lead at Microsoft Corporation based out of their HQ in Redmond.

Venue: SP Jain Global School of Management, 1st Floor, Kohinoor City Mall, Premier Rd, Kurla, Mumbai

Date – 5 Dec 2016

Time – 10:30 am

Location – Mumbai 

Register Here

What is CNTK?

Cognitive Toolkit is deep learning toolkit by Microsoft. It supports feed-forward, convolutional, and recurrent network for speech, image and text workloads also in combination. Popular network types are supported either natively or can be described as a CNTK configuration. It can also scale to multiple servers.

 

What is the session about?

The session will explore Microsoft’s open source computation-graph based on deep learning toolkit CNTK. It is used to train and evaluate deep neural networks found in Microsoft products like Cortana.

 

What will you learn?

Learn how  CNTK is used to observe deep learning needs, what they can and cannot do. Understand the workflow of the CNTK and how to implement different algorithms using CNTK. To provide you hands-on experience the session will be followed by a tutorial.

 

Why should you attend this event?

  1. Build networking opportunities.
  2. Learn from experts from Microsoft.
  3. No registration fee
CNTKCognitive Toolkitconvolution neural networksdeep learningDeep learning toolkitimplementation of neural networksMicrosoftneural networksSP Jain School of Global Management
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