Deep Learning with Python Training

Deep Learning with Python Training in Kathmandu, Nepal

Duration: 3.5 Months
Career: Data Scientist
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Description

Deep Learning with Python Training in Nepal

Broadway envisions playing a productive role in the development process of AI by offering courses on Deep Learning with Python programming in Nepal. Deep learning has taken the world by storm for these past few years. It was designed to imitate a human brain, in simple words to find patterns in behavior through neural networks resembling the features of the brain. Deep learning, machine learning or AI has already advanced to the point where intelligent chatbots, self-driving cars, and virtual assistance exist.

As a consequence of Python programming language, which is supposedly a roadmap to deep learning, the world has grown to favor it, including Broadway Infosys Nepal that has been offering job-oriented deep learning with python training in Nepal. This course teaches IT fanatics to use Keras 2.0, the latest version of the python program to contribute their knowledge in the field of Deep learning.

Benefits of Deep Learning with Python Training

Deep learning with python is beneficial for the following reasons.

  • Students can learn AI easily and efficiently using Python.
  • Provides an individual with option to choose between oops approach and scripting.
  • Candidates can achieve the basic concepts of Tensorflow and CNTK.
  • Gives the student a creative enhancement for learning AI
  • Easy to land a job due to the tremendous demand of AI domain experts.
  • Wider career opportunities

Python is easily chosen over other programming languages, for instance, C++ and Java, all thanks to its easily readable and understandable nature. Moreover, for any candidates willing to earn a platform in AI, this course is a stepping stone as it supports deep learning in every way possible. Therefore, please contact us today to learn more about this course which suits you best and earns you key skills.

Benefits of Deep Learning with Python Training at Broadway

Undergoing the course of Deep learning with Python at Broadway benefits you in the following ways.

  • Learn from qualified and exceptional IT experts.
  • Experience the process of learning in a well equipped training lab.
  • Learn at a reasonable cost with the scholarship opportunity.
  • Experience the sessions alongside your professional growth.
  • Assurance of internship opportunities after completing the training.
  • Expand your professional network and ideas.

Broadway Infosys Nepal invites eligible students to join the training sessions on Deep Learning with Python. Enroll your name at the earliest in order to appear in the training routine. Therefore, do it now and do it wisely so you do not have to wait until the next session starts.

Deep Learning with Python Training - Outlines
    Course Outline: Python Programming
  • Basic Syntax

    • Environment setup
    • The python programming language
    • What is program?
    • What is debugging?
  • Variables, Statement and Statements

    • Values and types
    • Variables
    • Variable names and keywords
    • Operators and operands
    • Expressions and statements
    • Interactive mode and script mode
    • Order of operations
    • String operations
    • Comments
    • Debugging
  • Function

    • Function calls
    • Type conversion functions
    • Math functions
    • Composition
    • Adding new functions
    • Definitions and uses
    • Flow of execution
    • Parameters and arguments
    • Variables and parameters are local
    • Stack diagrams
    • Fruitful functions and void functions
    • Why functions?
    • Importing with from
    • Debugging
  • Condition and Recursion

    • Modulus operator
    • Boolean expressions
    • Logical operators
    • Conditional execution
    • Alternative execution
    • Chained conditionals
    • Nested conditionals
    • Recursion
    • Stack diagrams for recursive functions
  • Fruitful Functions

    • Return values
    • Incremental development
    • Composition
    • Boolean functions
    • More recursion
    • Leap of faith
    • Checking types
    • Debugging
  • Iteration

    • Multiple assignments
    • Updating variables
    • The while statement
    • Break
    • Debugging
    • For loop
  • String

    • A string is a sequence
    • Len
    • Traversal with a for loop
    • String slices
    • Strings are immutable
    • Searching
    • Looping and counting
    • String methods
    • The in operator
    • String comparison
    • Debugging
  • List

    • A list is a sequence
    • Lists are mutable
    • Traversing a list
    • List operations
    • List slices
    • List methods
    • Map, filter and reduce
    • Deleting elements
    • Lists and strings
    • Objects and values

     Aliasing  List arguments  Debugging

  • Dictionary

    • Dictionary as a set of counters
    • Looping and dictionaries
    • Reverse lookup
    • Dictionaries and lists
    • Memos
    • Global variables
    • Long integers
    • Debugging
  • Tuple

    • Tuples are immutable
    • Tuple assignment
    • Tuples as return values
    • Variable-length argument tuples
    • Lists and tuples
    • Dictionaries and tuples
    • Comparing tuples
    • Sequences of sequences
    • Debugging
  • Set

    • Usage
    • Union, Difference
    • Available methods
  • Exception Handling

    • Introduction
    • Exceptions versus Syntax Errors
    • Raising an Exception
    • The AssertionError Exception
    • The try and except Block: Handling Exceptions
    • The else Clause
  • Files

    • Persistence
    • Reading and writing
    • Format operator
    • Filenames and paths
    • Catching exceptions
    • Databases
    • Writing modules
    • Debugging
  • CSV

    • Introduction, Application and Usage
    • Reader and writer
    • DictReader and DictWriter
    • Simple CSV processing using functional programming
  • Pandas

    • Introduction to Pandas
    • DataFrame Data Structure
    • DataFrame Indexing and Loading
    • Querying a DataFrame
    • Indexing Dataframes
    • Manipulating DataFrame
  • Database with Python

    • Installations
    • Introduction, Application and Usage
    • Basic Structured Query Language
    • CRUID operations
  • Basic Data Visualization

    • Principles of Information Visualization
    • Visualizing Data Using Spreadsheets
    • Matplotlib
    • Plotly
    • Scatterplots
    • Line Plots
    • Bar Charts
    • Histograms
    • Plotting with Pandas
  • Class and Objects

    • User-defined types
    • Attributes
    • Real World Example
    • Instances as return values
    • Objects are mutable
    • Copying
    • Debugging
  • Classes and Methods

    • Object-oriented features
    • The self
    • Printing objects
    • The init method
    • The __str__ method
    • Other special methods
    • Operator overloading
    • Type-based dispatch
    • Polymorphism
    • @staticmethod
    • Debugging
  • Callable and Non-Callable Object

    • Introduction
    • Checking callable or not
    • Decorators
    • Creating and using decorators
  • Inheritance

    • Introduction
    • Example
    • Class attributes
    • Private, Protected and Public
    • Multiple Inheritance
    • Class diagrams
    • Debugging
    • Data encapsulation
  • GIT

    • Installing Git
    • status, log, commit push, pull commands
    • Branch, Tags and Multiple remote concept and Implementation
    • checkout, reset, rebase, merge concept
    • Gitlab vs Github vs Bitbucket
  • Tools

    • Trello, Slack, Jira
  • Bonus

    • Advanced Strings, Date & Time
    • Python os, re, sys
    • GUI basics: Tkinter, Tcl/Tk
    • Comprehensions: List, Dictionary
    • CSV, Json, XML, SQLite with Python
    • Jupyter NoteBook
    • Data Streaming using Big Data Technologies like Kafka.
  • Final Project

    As per the recommendation of students, one of the following projects will be done by the instructor themselves!

    • Web Scraping project (includes handling web scraping tools, proper file handling and implementation of sql)
    • GUI project (any desktop application e.g: calculator, data entry application)
  • AI - Deep Learning in Python (Syllabus)
  • Prerequisites for Deep Learning

    • Introduction to Google co-lab and Anaconda
    • Numpy
    • Pandas
    • Plotting and Charting

    Project1: Mini Project

  • Frameworks

    • Tensorflow
    • Pytorch
    • Keras
    • Theano
  • Introduction to Computer Vision

    • Computer Vision Overview
    • Image Formation
    • History
  • Image Processing and Feature Detection

    • Introduction to Open CV
    • Different Types of Filter
    • Feature Detection
    • Edge Detection
    • Haar-like Features
    • Frequency Domain Analysis 

    Project2: Face Detection

  • Introduction to Deep Learning

    • Introduction to Deep Learning
    • History
    • Why Deep Learning Taking Off
    • Building Block of deep Learning
    • Application of Deep Learning
  • Artificial Neural Networks

    • Multilayer Perceptron
    • Back Propagation
    • Working Of neural network
    • Adjusting the weights
    • Gradient Descent
    • Stochastic Gradient Descent
  • Convolution Neural Network

    • Foundations of Convolutional Neural Network
    • CNN Architecture
    • Convolution Operation
    • ReLU Layer
    • Pooling
    • Flattening
    • Full Connection
    • Softmax & Cross-Entropy
    • Summary
  • Data Preprocessing

    • Get the dataset
    • Importing the Libraries
    • Importing the Dataset
    • Splitting the Dataset into the Training set and Test set
    • Feature Engineering
  • Image Classification and Object Recognition

    • Traditional Computer Vision
    • Image classification using Deep Learning
    • Binary and Multi class Image classification
    • Deep learning in Object Detection  SSD,YOLO

    Project3: Binary and Multi Image Classification

    Project4: Object Recognition

  • Generative Adversarial Network

    • The Idea Behind GANs
      • How Do GANs Work?
    • Generator
    • Discriminator
    • Generative Adversarial Networks Representation
    • Mathematical Details About GANs
      • Applications of GANs
    • Current Research On GAN

    Project 5: Image Creation with GAN

  • Introduction to Natural Language Processing

    • NLP Overview
    • Use of NLP
    • Library and Frameworks
    • Why NLP is difficult?
  • Natural Language Processing Basics

    • Spacy Basics
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop Words
      • Word segmentation
    • Part-of-speech tagging
  • Deep Learning for NLP

    • Recurrent Neural Network(RNN)
    • Encoder
    • Decoder
    • LSTM
    • Attention
    • Sequence to Sequence Models
    • Sequence to Sequence Models Architecture
    • Current research on NLP

    Project6: Text Classification

    Project7: Sentiment Analysis

  • Use case of Natural Language Processing

    • Speech Recognition
    • Natural Language Understanding
    • Natural Language Generation
    • Attention Mechanism
  • Deep Reinforcement Learning

    • Foundations of Reinforcement Learning
    • Policy-Based Methods
    • Value-Based Methods
    • Policy-Based Methods
    • Deep Q-Learning
    • Applications
  • GPUs and Cloud Computing

    • Why GPU is needed?
    • Google Cloud Platform
    • Amazon Web Serves
    • Floyd Hub
    • GPU Hardware
  • Case Study

    • Alpha Go
    • Self-Driving Car
    • IBM Watson
    • Amazon Alexa
    • Face Book Artificial Intelligence
    • Google Deep Mind
  • Project Works

    • Project 1: Mini Project
    • Project 2: Face Detection
    • Project 3: Binary and Multi Image Classification
    • Project 4: Object Recognition
    • Project 5: Image Creation with GAN
    • Project 6: Text Classification
    • Project 7: Sentiment Analysis

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