Deep Learning Course (with TensorFlow) in Berlin, Germany

Name: Deep Learning Course (with TensorFlow) in Berlin, Germany
Address: Berlin, Berlin,Germany
Telephone: +44 20 3627 9615
Price range: 799.0000

Co-Developed with IBM

153 Ratings

2070 Learners

Simplilearn’s Deep Learning with TensorFlow course has been crafted by industry experts and aligned with the latest best practices to transform you into deep learning expert. You’ll learn to master deep learning concepts and the TensorFlow open source framework, implement deep learning algorithms, build artificial neural networks, and traverse layers of data abstraction to understand the power of data and prepare you for an exciting career in deep learning.

Key Features MONEY BACK GUARANTEE

How this works :

At Simplilearn, we greatly value the trust of our patrons. Our courses are designed to deliver an effective learning experience, and have helped over half a million find their professional calling. But if you feel your course is not to your liking, we offer a 7-day money-back guarantee. Just send us a refund request within 7 days of purchase, and we will refund 100% of your payment, no questions asked!

For Instructor Led Training :

Raise refund request within 7 days of commencement of the first batch you are eligible to attend. Money back guarantee is void if the participant has accessed more than 25% content of an e-learning course or has attended Online Classrooms for more than 1 day.

40 hours of instructor-led training
Real-life industry based projects
24*7 support with dedicated project mentoring sessions
Flexibility to choose classes
Dedicated mentoring session from our Industry expert faculties

For individuals

Online Classroom Flexi-Pass

What is Flexi Pass?

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90 days of flexible access to instructor-led online training classes in Berlin

Lifetime access to high-quality self-paced e-learning content and live class recordings
24x7 learner assistance and support

Weekend class starting 26th Oct
Weekend class starting 16th Nov

Show all classes View recording of previous class

For business

Corporate Training Solutions

Blended learning delivery model (self-paced eLearning and/or instructor-led options)
Flexible pricing options
Enterprise grade Learning Management System (LMS)
Enterprise dashboards for individuals and teams
24x7 learner assistance and support

Course Description

What are the course objectives?

Deep Learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course with TensorFlow is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.

Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
What skills will you learn with this Deep Learning course?
By the end of this deep learning course with TensorFlow, you will be able to accomplish the following:
- Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
- Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
- Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
- Build deep learning models in TensorFlow and interpret the results
- Understand the language and fundamental concepts of artificial neural networks
- Troubleshoot and improve deep learning models
- Build your own deep learning project
- Differentiate between machine learning, deep learning and artificial intelligence
Who should take this Deep Learning with TensorFlow course?
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
- Software engineers
- Data scientists
- Data analysts
- Statisticians with an interest in deep learning
What projects are included in this Deep Learning Online Training Course?

The course includes the following industry-based project. Successful evaluation of the following project is a part of the certification eligibility criteria:

Project 1: Pet Classification Model Using CNN

In this project, you build a CNN model that classifies the given pet images correctly into dog and cat images. The code template is given with essential code blocks. TensorFlow can be used to train the data and calculate the accuracy score on the test data.
What are the prerequisites for this Deep Learning online course?
Participants in this Deep Learning online course should have:
- Familiarity with programming fundamentals
- Fair understanding of basics of statistics and mathematics

Course Preview

Deep Learning with TensorFlow
- 1. Introduction to TensorFlow
  - Intro to TensorFlow
  - Computational Graph
  - Key highlights
  - Creating a Graph
  - Regression example
  - Gradient Descent
  - TensorBoard
  - Modularity
  - Sharing Variables
  - Keras
- 2. Perceptrons
  43:03
  - What is a Perceptron
    43:03
  - XOR Gate
- 3. Activation Functions
  20:52
  - Sigmoid
  - ReLU
    15:20
  - Hyperbolic Fns
    05:32
  - Softmax
- 4. Artificial Neural Networks
  1:13:08
  - Introduction
    59:50
  - Perceptron Training Rule
    13:18
  - Gradient Descent Rule
- 5. Gradient Descent and Backpropagation
  59:30
  - Gradient Descent
    59:30
  - Stochastic Gradient Descent
  - Backpropagation
  - Some problems in ANN
- 6. Optimization and Regularization
  - Overfitting and Capacity
  - Cross Validation
  - Feature Selection
  - Regularization
  - Hyperparameters
- 7. Intro to Convolutional Neural Networks
  - Intro to CNNs
  - Kernel filter
  - Principles behind CNNs
  - Multiple Filters
  - CNN applications
- 8. Introduction to Recurrent Neural Networks
  - Introduction to RNNs
  - Unfolded RNNs
  - Seq2Seq RNNs
  - LSTM
  - RNN applications
- 9. Deep Learning applications
  - Image Processing
  - Natural Language Processing
  - Speech Recognition
  - Video Analytics
Free Course

Math Refresher
- Math Refresher
  30:36
  - Math Refresher
    30:36
Free Course

Deep Learning Fundamentals
- Lesson 1 - Learning Objectives
  - Learning Objectives
- Lesson 2 - Introduction to Deep Learning
  22:31
  - Learning Objectives
  - 1.1 Deep Learning: The Series Introduction
    03:46
  - 1.2 What is a Neural Network
    06:28
  - 1.3 Three Reasons to go Deep
    03:56
  - 1.4 Your choice of Deep Net
    02:58
  - 1.5 An Old Problem
    05:23
- Lesson 3 - Deep Learning Models
  22:56
  - Learning Objectives
  - 2.1 Restricted Boltzmann Machines
    04:50
  - 2.2 Deep Belief Nets
    04:31
  - 2.3 Convolutional Nets
    08:15
  - 2.4 Recurrent Nets
    05:20
- Lesson 4 - Additional Deep Learning Models
  19:13
  - Learning Objectives
  - 3.1 Autoencoders
    03:51
  - 3.2 Recursive Neural Tensor Nets
    05:49
  - 3.3 Use Cases
    09:33
- Lesson 5 - Deep Learning Platforms & Libraries
  25:37
  - Learning Objectives
  - 4.1 What is a Deep Net Platform?
    03:41
  - 4.2 H2O.ai
    03:42
  - 4.3 Dato GraphLab
    03:32
  - 4.4 What is a Deep Learning Library?
    01:58
  - 4.5 Theano
    03:21
  - 4.6 Caffe
    02:48
  - 4.7 Tensorflow
    06:35
  - Unlocking IBM Certificate
Free Course
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Exam & Certification

What do I need to do to unlock my Simplilearn certificate?
To obtain the Deep Learning with TensorFlow certification for Online Classroom, you will need to:
- Attend one complete batch
- Complete and attain evaluation of any one of the given projects
Who provides the certification and how long is it valid for?

Upon successful completion of the Deep Learning online training course, you will be awarded an industry-recognized course completion certificate from Simplilearn which has a lifelong validity.
What are the system requirements to attend the training sessions?
Minimum system requirements for attending this course are-
- 8 core processor
- 32 GB RAM

Course Advisor

Mike Tamir No. 1 AI and Machine Learning Influencer, Head of Data Science - Uber ATG

Named by Onalytica as the No.1 influencer in AI and Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

Reviews

A.Anthony Davis Kingston

The Simplilearn Data Scientist Master’s Program is an awesome course! You learn how to solve real-world problems, and the wide variety of projects give you hands-on experience to make you industry-ready. The lecturers are experts and share their knowledge energetically. Thank you for an excellent learning experience.

Abhishek Tripathi Bangalore

Good online content for data science. I completed Data Science with R and Python. The instructors have good knowledge on the subject. Self-learning videos help a lot, too. Thanks, Simplilearn.

Angiras Modak Associate System Engineer at IBM India Pvt. Ltd., Kolkata

Simplilearn is one of the best online training providers available. The trainer was really great in explaining the concepts to the minute detail and also gave multiple real-world examples. The course content was very informative. I understood the concept of CNN. Overall I really enjoyed the training a lot.

FAQs

What if I miss a class?

Simplilearn provides recordings of each class so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.
Who are the instructors and how are they selected?

All of our highly qualified trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.
How will the labs be conducted?

Simplilearn provides Integrated labs for all the hands-on and projects execution. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.
What is Global Teaching Assistance?

Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.
Is this live training or will I watch pre-recorded videos?

The Deep Learning online training is conducted through live streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each session you attend for your future reference. Classes are attended by a global audience to enrich your learning experience.
What is covered under the 24/7 Support promise?
We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.
What is Online Classroom training?
All of the classes are conducted via live online streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time.
Do you provide a money back guarantee for the training programs?
Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.
How do I enrol for the online training?
You can enrol for this Deep Learning online training on our website and make an online payment using any of the following options:
- Visa Credit or Debit Card
- MasterCard
- American Express
- Diner’s Club
- PayPal
Once payment is received you will automatically receive a payment receipt and access information via email.
If I need to cancel my enrollment, can I get a refund?
Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.
How can I learn more about this training program?
Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

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+44 20 3627 9615

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Deep Learning Course (with TensorFlow) in Berlin, Germany

Co-Developed with IBM

Online Classroom Flexi-Pass What is Flexi Pass? Loading..

Corporate Training Solutions

Course Description

What are the course objectives?

What skills will you learn with this Deep Learning course?

Who should take this Deep Learning with TensorFlow course?

What projects are included in this Deep Learning Online Training Course?

What are the prerequisites for this Deep Learning online course?

Course Preview

Deep Learning with TensorFlow

1. Introduction to TensorFlow

Intro to TensorFlow

Computational Graph

Key highlights

Creating a Graph

Regression example

Gradient Descent

TensorBoard

Modularity

Sharing Variables

Keras

2. Perceptrons

What is a Perceptron

XOR Gate

3. Activation Functions

Sigmoid

ReLU

Hyperbolic Fns

Softmax

4. Artificial Neural Networks

Introduction

Perceptron Training Rule

Gradient Descent Rule

5. Gradient Descent and Backpropagation

Gradient Descent

Stochastic Gradient Descent

Backpropagation

Some problems in ANN

6. Optimization and Regularization

Overfitting and Capacity

Cross Validation

Feature Selection

Regularization

Hyperparameters

7. Intro to Convolutional Neural Networks

Intro to CNNs

Kernel filter

Principles behind CNNs

Multiple Filters

CNN applications

8. Introduction to Recurrent Neural Networks

Introduction to RNNs

Unfolded RNNs

Seq2Seq RNNs

LSTM

RNN applications

9. Deep Learning applications

Image Processing

Natural Language Processing

Speech Recognition

Video Analytics

Math Refresher

Math Refresher

Math Refresher

Deep Learning Fundamentals

Lesson 1 - Learning Objectives

Learning Objectives

Lesson 2 - Introduction to Deep Learning

Learning Objectives

1.1 Deep Learning: The Series Introduction

1.2 What is a Neural Network

1.3 Three Reasons to go Deep

1.4 Your choice of Deep Net

1.5 An Old Problem

Lesson 3 - Deep Learning Models

Learning Objectives

2.1 Restricted Boltzmann Machines

2.2 Deep Belief Nets

Online Classroom Flexi-Pass

What is Flexi Pass?

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