Deep Learning concepts has been in the limelight for some time. In this program, comprehend the concepts of Deep Learning using PyTorch – a recent deep learning library. You’ll gain hands-on experience using PyTorch to train neural networks, perform image classification, and apply object detection to both images and real-time video in this program. This certified program on Deep Learning using PyTorch focusses mainly on the DL Concepts through PyTorch Libraries. At the end of the course, the participants will gain unlimited exposure and will effectively learn and deploy PyTorch Libray for custom project. development
Total Duration: 15 Hrs Modules: 4 Assignments- 4
Module 1: Introduction to Deep learning
Module 2: Introduction to PyTorch
Module 3: Neural Network Designs using Torch
Module 4: Torch vision
Module 1: Introduction to Deep learning
Key Learning Objectives: Get complete insight into the domain of Deep Learning – Its concepts , Algorithms and applications. Use the Open Source Python Programming Language to comprehend and analyse the concepts and applications of DL
Tools Covered: Google Colab / IDLE /
Lesson 1: Intro to Deep learning
What is Deep learning? -What is neural Network? -Types of neural Networks-Different packages and tools used for deep Learning-Applications of deep learning
Lesson 2: Intro to python programming
Intro to python Language-Applications of python Language-Package installations in python-fundamentals of python.
Module 2: Introduction to PyTorch
Key Learning Objectives
Get Extensive insight into the PyTorch – A Unique and the most comprehensive Library for Deep Learning and with Python API’s. Sponsored by the Facebook Research Team , PyTorch comes out with the most desirable and user friendly libraries for DL Concepts and utilisation. Get to Know More.
Module 2: PyTorch introduction
PyTorch Intro – Installation – About Torch Audio- Torch text – Torch vision- Torchserve – Basic Concepts and Applications
Lesson 4: Working with PyTorch Tensor
Creating tensors- Data type allocation – Tensor Creation – Application Analysis – Custom Project Design using PyTorch
Assignment 1- Create a basic tensor with relevant data type.
Module 3: Neural Network using PyTorch
Key Learning Objectives:
Neural Networks are an absolute programming paradigm for learning and analysing observatory data for custom applications. In this module, learn and practice to create multiple layers of Neural Networks using Python and PyTorch Library. With assisted hands on Sessions, get complete step by step assisted procedural approach towards NN Implementation using PyTorch
Lesson 5: Linear layers & Dropout Layer
Linear – Identity – Bilinear – Lazy linear. Dropout – Dropout 2d – Dropout 3d – Alpha dropout
Assignment 2 – Create a dropout 3d layer using random values.
Lesson 6: Design of Transformer Layer
Transformer – Transformer Encoder – Transformer Decoder – Transformer Encoder Layer – Transformer Decoder layer
Lesson 7: Vision Layer NN using PyTorch
Pixel shuffle – Pixel Unshuffle – Up sample – Up sampling Nearest 2d – Up sampling Bilinear 2d
Assignment 3 – Create Up sampling Nearest 2d using random values.
Lesson 8: Normalization Layer Design & Metrics
Batch normalization 1d – Batch normalization 2d – Batch normalization 3d – Lazy Batch normalization 1d – Batch normalization 2d – Batch normalization 3d – Group Normalization – Sync Batch Normalization – Instance Normalization 1d – Instance Normalization 2d – Instance Normalization 3d – Layer Normalization – Local response Normalization
Assignment 4 – Create a Group Normalization using random values.
Module 4: Torch vision
Key Learning Objectives:
This module will focus on application of Data Set based Neural Network Project Design using PyTorch and TorchVision. Mentor enabled learning module focuses on the Image Based Working on NN Design and applications of PyTorch for Image Based Processing and data analysis .
Lesson 9: Torch vision dataset
About torch vision – how to import dataset in torch vision-CNN
Lesson 10: Torch vision model
About different models in Torch vision – how to work with simple model in torch vision-Alexnet
Assignment: Classify image using Torch vision
Projects:
1.Create multiple NN layers using pytorch.
2.Multiple image classification using Alexnet and PyTorch.
3.Image classification using transfer learning and Torch vision
Pantech eLearning
Agile Project Expert
24 Comments
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Here is teaching style is very good and very easily explain.
I have completed my pytorch course.thank you sir
Really I enjoyed live classes and it is very easy to understand
Very well covered the basics to projects !
good teaching and concepts are explained well
Thankyou so much for your valuable session.
Amazing Internship Experience, valuable assignment
ThankYou For this Amazing Session.
Learned lot of new things
Best Teaching and good explanation about modules
I had very amazing experience. Learnt many new things. Valuable Internship.
I thoroughly enjoyed my internship this summer and now have very valuable experience…thank you
I thoroughly enjoyed my internship and now have very valuable experience…Thank you for this platform
I genuinely learned lot of new things from this course and also from Jishnu sir.
Thank you pantech
Very Good Internship provided… Thank you for this
Had very valuable sessions… Thank you for this platorm
Amazing Internship Experience, valuable assignment
Totally enjoyed and learned a lot in a comfortable environment. The style and the knowledge of the presenter, Jishnu sir, was totally amazing.