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Deep Learning With Python Novice To Pro

Course

DEEP LEARNING WITH PYTHON NOVICE TO PRO

Category

Python IT Training

Eligibility

Working Professionals and Freshers

Mode

Online and Classroom Sessions

Batches

Week Days and Week Ends

Duration :

45 Days

Python What will you learn?

•How to create a Python Project.
•Master Python concepts from the ground up
•Learn Python from scratch. Code like a PRO
•How to apply the Python rules in different situations.
•Learn about Python in a step by step approach
•Master the latest version of Python and create real projects
•One stop solutions and step by step process for learning Python
•Build a strong knowledge base on Python from Scratch to Advanced level
•Learn and understand the fundamentals of Python and how to apply it to web development.

deep learning with python novice to pro Course Features

•24 × 7 = 365 days supportive faculty
•Course has been framed by Industry experts
•Doubt clarification in class and after class
•Regular Brush-up Sessions of the previous classes
•We Also provide Case studies for Online Training Courses
•Courseware includes reference material to maximize learning.
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Who are eligible for Python

•Automative design eng, Chasis Brakes, UGNX, Electrical, UI, Mobile Testing, .NET Lead, Cognos Developer, Java j2ee, Core Java, Automation Testing
•Java Developer, Php, Sales Management, Product Management, Software Services, Ui Development, MySQL, MongoDB, Nginx, NoSQL, Solr, Elastic Search, ApacheJava/j2ee, Microsoft, Erp, Cloud, Qa/testing, Automation Testing, Analytics, Machine Learning, Artificial Intelligence, Agile Project Management, Mobility
•QT Developer, STB Domain, CAS, UX DESIGNER, UI Developer, HTML5, CSS3, JAVAScript, JQUERY, FIREWORKS, Adobe Photoshop, Illustratot, Embedded C++
•Sharepoint, Java J2ee, Oracle EBS, Peoplesoft, Oracle, Data, UI/ UX Designers/ Developers, HTML Developer, .net Developers, Mainframe, MBBS, AV Engineer, Audio

DEEP LEARNING WITH PYTHON NOVICE TO PRO Topics

•Python Deep Learning for Beginners
•The Course Overview
•A Brief History of Deep Learning
•Deep Learning Today
•Tools Requirements and Setup
•Exploring Supervised Learning
•Representational Learning and Feature Engineering
•Linear Regression
•The Perceptron
•Feedforward Networks
•Backpropagation
•Neural Networks from Scratch
•Overfitting and Regularization
•Understanding CNNs
•Implementing a CNN
•Deep CNNs
•Very Deep CNNs
•Batch Normalization
•FineTuning
•Semantic Segmentation
•Fully Convolutional Networks
•Recurrent Neural Networks
•LSTM and Advancements
•Building a CNN to Detect General Images
•Training and Deploying on a Cluster
•Comparison of DL Frameworks
•Exciting Areas for Upcoming Research
•Test Your Knowledge
•RealWorld Python Deep Learning Projects
•What Types of Problems Can You Solve Using Deep Learning
•Installing Essential DL Tools
•Based on Past Data Predicting the Number of Airline Passengers
•Getting and Preparing Airline Data
•Building Your Multilayer Perceptron Model
•Training and Testing Your Model
•Making Predictions and Whats Next
•End Goal Label a Given Tweet Short Text as Negative or Positive
•Dataset Overview
•Preparing Data for Sentiment Analysis
•What Are Word Embeddings and Why They Are Important When Working with CNNs
•Building Your CNN Model for Text Classification
•Detecting Mean Tweets Using Your Model and Whats Next
•Detect Whether an Image Contains a Smile with High Accuracy
•Getting and Preparing Data for Smile Detection
•Building Your CNN Model for Smile Detection
•Detecting Smiles with Your Model and Whats Next
•Predict the Closing Stock Price of a Given Company for the Next Day
•Getting and Preparing Stock Prices Data
•Building Your LSTM Model for Price Prediction
•Detecting Closing Stock Price with Your Model and Whats Next
•Python Deep Learning Solutions
•Understanding TensorFlow Keras and PyTorch Framework
•Deep Learning Using CNTK and Gluon Framework
•Implementing Single and MultiLayer Neural Network
•Experiment with Activation Functions Hidden Layers and Hidden Units
•Autoencoder Loss Function and Optimizers
•Overfitting Prevention Methods
•Optimization Techniques for CNNs
•Experimenting with Different Types of Initialization
•Implementing Simple RNN and LSTM
•Implementing GRUs and Bidirectional RNNs
•Implementing Generative Adversarial Networks
•Computer Vision Techniques
•Detecting Facial Key Points and Transferring Styles
•Hyper Parameter Selection and Tuning
•Speech Recognition
•Time Series and Structured Data
•Visualizing and Analysing Network
•Freezing and Storing the Network
•Using InceptionV and ResNet Model
•Leveraging VGG Model and Fine Tuning
•Tools, Requirements, and Setup
•Fine-Tuning
•Real-World Python Deep Learning Projects
•What Types of Problems Can You Solve Using Deep Learning?
•Based on Past Data, Predicting the Number of Airline Passengers
•Making Predictions and What’s Next?
•End Goal – Label a Given Tweet (Short Text) as Negative or Positive
•What Are Word Embeddings and Why They Are Important When Working with CNNs?
•Detecting Mean Tweets Using Your Model and What’s Next?
•Building Your CNN Model for Smile Detection.
•Detecting Smiles with Your Model and What’s Next?
•Detecting Closing Stock Price with Your Model and What’s Next?
•Understanding TensorFlow, Keras and PyTorch Framework
•Implementing Single and Multi-Layer Neural Network
•Experiment with Activation Functions, Hidden Layers, and Hidden Units
•Autoencoder, Loss Function, and Optimizers
•Using InceptionV3 and ResNet50 Model