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Pytorch Deep Learning

Course

PYTORCH DEEP LEARNING

Category

Deep Learning Training Insitute

Eligibility

Working Professionals and Freshers

Mode

Both Classroom and Online Classes

Batches

Week Days and Week Ends

Duration :

1.5  hrs in weekdays and 3hrs during Weekend

Deep Learning Objectives

•How to create a Deep Learning Project.
•Learn Deep Learning proficiently in a structured fashion.
•Learn to how to code and deploy Deep Learning
•Students will learn how to build apps using Deep Learning.Learn How To Create Your Deep Learning In Easy Steps
•You’ll learn how to solve well known problems in Deep Learning.
•Beginner to Advance Level: Learn to Plan, Design and Implement Deep Learningyou will be confident in your skills as a Developer / designer
•You will be able to develop top class apps and think like a programmer

pytorch deep learning Course Features

•Career guidance providing by It Expert
•Certificate after completion of the course
•Doubt clarification in class and after class
•We enage Experienced trainers for Quality Training
•60+ Hours of Intensive Classroom & Online Sessions
•Courseware includes reference material to maximize learning.
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•We help the students in building the resume boost their knowledge by providing useful Interview tips

Who are eligible for Deep Learning

•C, asp.net vb.net c# c c++, Java Developer, Php Developer, dot net c# asp.net vb.net
•Java Developer, java j2ee jsp servlets ejb, plsql, Unix Scripting, c, c++, dotnet
•Java, Cc++ Developers, .Net Developers, Python Developers, Php Developers, Qa Test Engineers, Sharepoint Developers Veritas Engineers.
•QT Developer, STB Domain, CAS, UX DESIGNER, UI Developer, HTML5, CSS3, JAVAScript, JQUERY, FIREWORKS, Adobe Photoshop, Illustratot, Embedded C++
•Software Development, .net, java, Asp.net, Sql Server, database, Software Testing, javascript, Agile Methodology, Cloud Computing, html, application

PYTORCH DEEP LEARNING Syllabus

Introduction
•Welcome
•Overview and Outline
•Where to get the Code
•Google Colab
•Intro to Google Colab, how to use a GPU or TPU for free
•Uploading your own data to Google Colab
•Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
•Machine Learning and Neurons
•What is Machine Learning?
•Regression Basics
•Regression Code Preparation
•Regression Notebook
•Moore’s Law
•Moore’s Law Notebook
•Linear Classification Basics
•Classification Code Preparation
•Classification Notebook
•Saving and Loading a Model
•A Short Neuroscience Primer
•How does a model “learn”?
•Model With Logits
•Train Sets vs. Validation Sets vs. Test Sets
•Feedforward Artificial Neural Networks
•Artificial Neural Networks Section Introduction
•Forward Propagation
•The Geometrical Picture
•Activation Functions
•Multiclass Classification
•How to Represent Images
•Code Preparation (ANN)
•ANN for Image Classification
•ANN for Regression
•Convolutional Neural Networks
•What is Convolution? (part 1)
•What is Convolution? (part 2)
•What is Convolution? (part 3)
•Convolution on Color Images
•CNN Architecture
•CNN Code Preparation (part 1)
•CNN Code Preparation (part 2)
•CNN Code Preparation (part 3)
•CNN for Fashion MNIST
•CNN for CIFAR-10
•Data Augmentation
•Batch Normalization
•Improving CIFAR-10 Results
•Recurrent Neural Networks, Time Series, and Sequence Data
•Sequence Data
•Forecasting
•Autoregressive Linear Model for Time Series Prediction
•Proof that the Linear Model Works
•Recurrent Neural Networks
•RNN Code Preparation
•RNN for Time Series Prediction
•Paying Attention to Shapes
•GRU and LSTM (pt 1)
•GRU and LSTM (pt 2)
•A More Challenging Sequence
•RNN for Image Classification (Theory)
•RNN for Image Classification (Code)
•Stock Return Predictions using LSTMs (pt 1)
•Stock Return Predictions using LSTMs (pt 2)
•Stock Return Predictions using LSTMs (pt 3)
•Other Ways to Forecast
•Natural Language Processing (NLP)
•Embeddings
•Neural Networks with Embeddings
•Text Preprocessing (pt 1)
•Text Preprocessing (pt 2)
•Text Preprocessing (pt 3)
•Text Classification with LSTMs
•CNNs for Text
•Text Classification with CNNs
•Recommender Systems
•Recommender Systems with Deep Learning Theory
•Recommender Systems with Deep Learning Code Preparation
•Recommender Systems with Deep Learning Code (pt 1)
•Recommender Systems with Deep Learning Code (pt 2)
•Transfer Learning for Computer Vision
•Transfer Learning Theory
•Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
•Large Datasets
•2 Approaches to Transfer Learning
•Transfer Learning Code (pt 1)
•Transfer Learning Code (pt 2)
•GANs (Generative Adversarial Networks)
•GAN Theory
•GAN Code Preparation
•GAN Code
•Deep Reinforcement Learning (Theory)
•Deep Reinforcement Learning Section Introduction
•Elements of a Reinforcement Learning Problem
•States, Actions, Rewards, Policies
•Markov Decision Processes (MDPs)
•The Return
•Value Functions and the Bellman Equation
•What does it mean to “learn”?
•Solving the Bellman Equation with Reinforcement Learning (pt 1)
•Solving the Bellman Equation with Reinforcement Learning (pt 2)
•Epsilon-Greedy
•Q-Learning
•Deep Q-Learning / DQN (pt 1)
•Deep Q-Learning / DQN (pt 2)
•How to Learn Reinforcement Learning
•Stock Trading Project with Deep Reinforcement Learning
•Reinforcement Learning Stock Trader Introduction
•Data and Environment
•Replay Buffer
•Program Design and Layout
•Code pt 1
•Code pt 2
•Code pt 3
•Code pt 4
•Reinforcement Learning Stock Trader Discussion
•VIP: Uncertainty Estimation
•Custom Loss and Estimating Prediction Uncertainty
•Estimating Prediction Uncertainty Code
•VIP: Facial Recognition
•Facial Recognition Section Introduction
•Siamese Networks
•Code Outline
•Loading in the data
•Splitting the data into train and test
•Converting the data into pairs
•Generating Generators
•Creating the model and loss
•Accuracy and imbalanced classes
•Facial Recognition Section Summary
•In-Depth: Loss Functions
•Mean Squared Error
•Binary Cross Entropy
•Categorical Cross Entropy
•In-Depth: Gradient Descent
•Gradient Descent
•Stochastic Gradient Descent
•Momentum
•Variable and Adaptive Learning Rates
•Adam
•Extras
•Links To Colab Notebooks
•Links to VIP Notebooks
•Setting up your Environment
•How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
•Windows-Focused Environment Setup 2018
•Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer
•Appendix / FAQ
•What is the Appendix?
•Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
•How to Code Yourself (part 1)
•How to Code Yourself (part 2)
•Proof that using Jupyter Notebook is the same as not using it
•How to Succeed in this Course (Long Version)
•What order should I take your courses in? (part 1)
•What order should I take your courses in? (part 2)
•BONUS: Where to get discount coupons and FREE deep learning material