Deep Learning with Python Training

Learn Deep Learning and its varied applications to grow your career in Big Data.

View Schedule Enquire Now
Deep Learning with Python

12000+

Trained

20+

Data Scientist

4.6

rating

Overview

With DL finding usage and relevance across multiple industries, an expertise in its functioning and ecosystem will help you strengthen your career prospects.

The Deep Learning with Python Training Course will familiarize you with the concepts and applications of Deep Learning. This course will take you from the basic concepts of DL to DL Model Optimization within a short span of time.

The Deep Learning with Python Training course has a Python Refresher Module incorporated into it, so you can revisit the Python Text Basics as per need. The course will introduce you to TensorFlow and DL Architectures Tour, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Autoencoders, Recurrent Neural Networks (RNN) and LSTM, Generative Neural Nets (GNN).

You’ll be able to not only understand the intuition behind Artificial Neural Networks, but also apply Artificial Neural Networks in practice.

Objectives of the course:

  • Learn the basic concepts and applications of Deep Learning
  • Learn about DL Libraries: Tensorflow, Keras, Pytorch and other Python frameworks for DL
  • Learn about Data Sourcing, pre-processing, feature engineering
  • Learn in detail about DL Architectures
  • Learn DL model optimization and quality metrics

Key Features of the course

High Quality Courseware

Get access to study material, white paper, mock exam and case studies prepared by the Big Data industry experts.

Interactive instructor-led training

Training sessions that meet the exact needs of every individual.

Practical training

Experience practical project development led by actual examples.

Relevant Study Material

Get relevant study material from Big Data Community Platform.

Extensive Learning

Learn better with case studies, activities and quizzes.

Self-Paced Learning

24 hours of self-paced learning.

Curriculum

Topics Covered

  • Pre-Training Assessment
    • MCQ based pre-training assessment on Python and Deep Learning.
  • Module: 1 Python Refresher
    • Data types Sequences
    • list
    • tuple
    • set
    • dictionary Mapping types Program structure Files and console
    • I/O Conditionals
    • Loops Built-ins
  • Python Text Basics
    • Introduction to Python Text Basics
    • Working with Text Files with Python
    • Working with PDFs
    • Regular Expressions
    • Assessment
  • Module: 2
    • Introduction to Deep Learning
      • Artificial Intelligence
      • Define Deep Learning
      • Neural Networks
      • Deep Learning Applications
    • Perceptron
      • What is a Perceptron
      • Logic Gates with Perceptrons
      • Activation Functions
      • Sigmoid
      • ReLU
      • Softmax
      • Hyperbolic Functions
  • Introduction to TensorFlow
    • Computational Graph
    • Artificial Intelligence
    • Key highlights
    • Creating a Graph
    • Regression example
    • Gradient Descent
    • Saving and Restoring Models
    • Tf.layers API
    • Keras-based networks
    • Tensor Board
  • Deep Neural Nets
    • Training Deep Neural Nets
    • Vanishing/Exploding Gradients
    • Xavier Initialization
    • Leaky ReLUs and ELUs
    • Batch Normalization
    • Transfer Learning
    • Unsupervised Pre-training
    • Optimizers
    • Regularization
    • Dropout
  • Convolutional Neural Networks
    • Intro to CNNs
    • Convolution Operation
    • Kernel filter
    • Feature Maps
    • Pooling
    • CNN Architecture
    • Implement CNN in TensorFlow
    • CNN Workflow for Keras
    • CNN With Keras
    • CNN on Image Data with Keras
  • Recurrent Neural Networks
    • Intro to RNNs
    • Unfolded RNNs
    • Basic RNN Cell
    • Dynamic RNN
    • Training RNNs
    • Time-series predictions
    • LSTM
    • Word Embeddings
    • Seq2Seq Models
    • Implement RNN in TensorFlow
    • Implement RNN with keras
  • Autoencoders With Convolution Neural Networks (CNN)
    • Introduction of Autoencoders to Deep Learning
    • Autoencoders With CNN- Tensorflow
    • Autoencoders With CNN- Keras
  • Post-Training Assessment
    • MCQ based post-training assessment to gauge current understanding on Deep Learning

Prerequisite

  • Basic knowledge of C\C++ Programming.
  • Intermediate Understanding of Python, Machine Learning and Sklearn Module of Python.
  • Eagerness to learn new innovative things.

Study material:

1. Course Materials are important as they are aligned with the course covered in class and can be easily downloaded from the Big Data Community Platform.

2. A Comprehensive Guide that covers all your doubts and includes a detailed reading list, accessible after course completion through the Learning Plan in the Big Data Community Platform.

Benefits Attendees Get:

  • A Deep Learning with Python certificate.
  • A Study Guide to help you navigate further hurdles, if any come up.
  • Posters to be used internally in Organization or projects (Softcopy, PDF format).
  • Case studies to review and relearn from.
  • A huge amount of practical exercises to keep you on your feet.
Read more Read less

What does Xebia provide differently?

Step into the realm of learning for an all-inclusive growth. Xebia is a pioneering IT consultancy and service provider that aims at Enterprise Development, Agile Development, DevOps, and Outsourcing Services.

World-class Training

World-class Training

Xebia Academy offers an intensive learning program and industry-specific training courses. It’s a globally acclaimed APMG International Partner for Big Data & Data Science training and certification courses. ReadmoreReadless

Boon To Career

Boon To Career

Xebia offers excellent consultancy, innovative tools, and continuous career growth. We will train you to become a Big Data and Data Science expert. ReadmoreReadless

Expert Advantage

Expert Advantage

Get trained by our In-House Data Science experts with an average of 18 years of experience: Data Science and Big Data Experts with extensive knowledge of data and AI.ReadmoreReadless

Flexible Learning

Flexible Learning

Pick the right course: You can choose a public class at our training centre, or learn with your colleagues in a customized, in-company training program, facilitated on-site at your location, anywhere in the world.ReadmoreReadless

Global Experience

Global Experience

18 years of professional training experience and trusted by over 1,00,000 professionals worldwide. Xebia Academy is the largest producer of Big Data and Data Science certifications globally.ReadmoreReadless

Global Experience

Hands-on And Practical Learning Experience

Our trainers are hands-on practitioners and provide interactive training sessions which let students master required skills in real-world scenarios, giving them an edge in the industry.ReadmoreReadless

Certification Process

  • 01

    Enroll for Deep Learning With Python Course

  • 02

    Attend the 24 hours of training

  • 03

    Get certified by Xebia Academy Global

Industry Connect

Who should attend this course?

People who usually take this course include:

  • Developers

  • Analytics Managers

  • Information Architects

  • Analytics Professionals

  • Software or data engineers interested in quantitative analysis with Python

  • Data analysts, economists or researchers

What skills will you learn in the course?

The Fundamentals

You’ll learn about the basic concepts of Deep Learning, and refresh your knowledge of PythonReadmoreReadless

Practical Implementation

You’ll learn to understand and apply Neural Nets. ReadmoreReadless

Production-Ready Solutions

You’ll learn to work with DL Libraries, including Tensorflow, Keras, Pytorch and other Python frameworks for DL ReadmoreReadless

Working with DL Architecture

You’ll learn to operate DL Architecture like Multi-Layer Perceptron, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks and LSTM, Generative Neural Nets.ReadmoreReadless

Learning Models

You’ll learn about Model Optimization and quality metrics in DL. ReadmoreReadless

Why should you attend this course?

By the end of this course, you’ll have an understanding of:

  • The basic concepts and applications of Deep Learning
  • DL Libraries: Tensorflow, Keras, Pytorch and other Python frameworks for DL
  • Data Sourcing, pre-processing, feature engineering
  • DL Architectures: Multi-Layer Perceptron, Convolutional Neural Networks,Autoencoders, Recurrent Neural Networks and LSTM, Generative Neural Nets
  • Model optimization and quality metrics in Deep Learning

Know your Trainers

Program Visual Library

FAQs

Along with an eagerness to learn new things, this course requires:

  • Basic knowledge of C\C++ Programming.
  • Intermediate Understanding of Python, Machine Learning and Sklearn Module of Python.

This course is designed for:

  • Developers
  • Analytics Managers
  • Information Architects
  • Analytics Professionals
  • Software or data engineers interested in quantitative analysis with Python
  • Data analysts, economists or researchers

The trainers at Xebia Academy Global are certified experts with an impressive experience and a passion for teaching.

To enroll for the course, you have to register at the Xebia Academy Global website. After registering for the Deep Learning with Python training, you will receive a confirmation email with practical information.

There is no exam that you need to appear for to receive the Deep Learning with Python certification. However, finishing 24 hours of training is a must.

The Deep Learning with Python certification is valid for a lifetime. You do not need to renew it.

The Deep Learning with Python certificate requires a total training of twenty four hours, which can be finished at the candidate’s own pace.

Library Image

Repositories of trending knowledge

Knowledge sources from Xebians to enlighten learners

View More
  • Library Image
  • Library Image
  • Library Image
  • Library Image

Stay updated about the latest courses

Register now to receive notifications of upcoming trainings and latest courses.