Deep Learning Course (with TensorFlow) in Singapore
Co-Developed with IBM
Simplilearn's Deep Learning with TensorFlow training in Singapore serves the purpose of turning you into an expert by providing insights into Deep Learning and TensorFlow concepts. TensorFlow is an open-source software library that is related to machine learning uses and the deep neural network. The tensorflow training course prepares you to become a deep learning scientist by training you in deep learning techniques like building artificial neural networks and traversing through layers of data abstraction to understand the power of data and implement algorithms.
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Deep Learning Course Overview
In this Deep Learning with Keras and TensorFlow course, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results and build your own deep learning project.
Tensorflow Training Key Features
At Simplilearn, we value the trust of our patrons immensely. But, if you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
- 34 hours of Blended Learning
- 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
Skills Covered
- Keras and TensorFlow Framework
- PyTorch and its elements
- Image Classification
- Artificial Neural Networks
- Autoencoders
- Deep Neural Networks
- Conventional Neural Networks
- Recurrent Neural Networks
- ADAM Adagrad and Momentum
Benefits
Deep learning is one of the latest technologies in AI and machine learning, used in smartphone apps, power grids, helping us find solutions to climate change, and more. This course can lead to lucrative roles in IT, healthcare, FinTech, e-commerce, and other industries.
- Designation
- Annual Salary
- Hiring Companies
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
Deep Learning Course Curriculum
Eligibility
Demand for skilled deep learning engineers is booming across a wide range of industries, making this Deep Learning with Keras and TensorFlow training well-suited for professionals at the intermediate to advanced level. We recommend this deep learning course particularly for Software Engineers, Data Scientists, Data Analysts and Statisticians with an interest in deep learning.
Pre-requisites
Participants in this Deep Learning with Keras and TensorFlow course should have familiarity with programming fundamentals, fair understanding of basics of statistics and mathematics and good understanding of machine learning concepts.
Course Content
Section 1 - Deep Learning with Tensor Flow (Self Learning)
PreviewLesson 2 - Introduction to Tensorflow
31:55Preview2.1 Learning Objectives
07:002.2 Introduction to TensorFlow
07:002.3 TensorFlow's Hello World
03:282.4 Tensorflow Hello World
2.5 Linear Regression With Tensorflow
2.6 Logistic Regression With Tensorflow
2.7 Activation Functions
2.8 Intro to Deep Learning
02:392.9 Deep Neural Networks
11:48
Lesson 3 - Convolutional Networks
21:513.1 Learning Objectives
3.2 Intro to Convolutional Networks
04:373.3 CNN for Classifications
04:093.4 CNN Architecture
13:053.5 Understanding Convolutions
3.6 CNN with MNIST Dataset
Lesson 4 - Recurrent Neural Network
24:43Preview4.1 Learning Objectives
03:064.2 The Sequential Problem
03:064.3 The RNN Model
05:284.4 The LSTM Model
05:254.5 Applying RNNs to Language Modeling
07:384.6 LTSM Basics
4.7 MNIST Data Classification With RNN/LSTM
4.8 Applying RNN/LSTM to Language Modelling
4.9 Applying RNN/LSTM to Character Modelling
Lesson 5 - Restricted Boltzmann Machines (RBM)
14:14Preview5.1 Learning Objectives
04:295.2 Intro to RBMs
04:295.3 Training RBMs
05:165.4 RBM MNIST
5.5 Collaborative Filtering With RBM
Lesson 6 - Autoencoders
17:206.1 Learning Objectives
04:516.2 Intro to Autoencoders
04:516.3 Applying RNNs to Language Modelling
07:386.4 Autoencoders
6.5 DBN MNIST
Lesson 1 - Welcome!
02:061.1 Welcome!
02:061.2 Learning Objectives
Lesson 7 - Course Summary
02:17Preview7.1 Course Summary
02:17Unlocking IBM Certificate
Section 2 - Deep Learning with Keras and Tensor Flow (Live Classes)
PreviewLesson 1 - Course introduction
Introduction
Lesson 2 - AI and Deep learning introduction
What is AI and Deep learning
Brief History of AI
Recap: SL, UL and RL
Deep learning : successes last decade
Demo & discussion: Self driving car object detection
Applications of Deep learning
Challenges of Deep learning
Demo & discussion: Sentiment analysis using LSTM
Fullcycle of a deep learning project
Key Takeaways
Knowledge Check
Lesson 3 - Artificial Neural Network
Biological Neuron Vs Perceptron
Shallow neural network
Training a Perceptron
Demo code: Perceptron ( linear classification) (Assisted)
Backpropagation
Role of Activation functions & backpropagation
Demo code: Backpropagation (Assisted)
Demo code: Activation Function (Unassisted)
Optimization
Regularization
Dropout layer
Key Takeaways
Knowledge Check
Lesson-end Project (MNIST Image Classification)
Lesson 4 - Deep Neural Network & Tools
Deep Neural Network : why and applications
Designing a Deep neural network
How to choose your loss function?
Tools for Deep learning models
Keras and its Elements
Demo Code: Build a deep learning model using Keras (Assisted)
Tensorflow and Its ecosystem
Demo Code: Build a deep learning model using Tensorflow (Assisted)
TFlearn
Pytorch and its elements
Key Takeaways
Knowledge Check
Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset
Lesson 5 - Deep Neural Net optimization, tuning, interpretability
Optimization algorithms
SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
Batch normalization
Demo Code: Batch Normalization (Assisted)
Exploding and vanishing gradients
Hyperparameter tuning
Interpretability
Key Takeaways
Knowledge Check
Lesson-end Project: Hyperparameter Tunning With Keras Tuner
Lesson 6 - Convolutional Neural Network
Success and history
CNN Network design and architecture
Demo code: CNN Image Classification (Assisted)
Deep convolutional models
Key Takeaways
Knowledge Check
Lesson-end Project: Image Classification
Lesson 7 - Recurrent Neural Networks
Sequence data
Sense of time
RNN introduction
LSTM ( retail sales dataset kaggle)
Demo code: Stock Price Prediction with LSTM (Assisted)
Demo code: Multiclass Classification using LSTM (Unassisted)
Demo code: Sentiment Analysis using LSTM (Assisted)
GRUs
LSTM Vs GRUs
Key Takeaways
Knowledge Check
Lesson-end Project: Stock Price Forecasting
Lesson 8 - Autoencoders
Introduction to Autoencoders
Applications of Autoencoders
Autoencoder for anomaly detection
Demo code: Autoencoder model for MNIST data (Assisted)
Key Takeaways
Knowledge Check
Lesson-end Project: Anomaly detection with Keras
- Free Course
Deep Learning Fundamentals
PreviewLesson 1 - Learning Objectives
Learning Objectives
Lesson 2 - Introduction to Deep Learning
22:31PreviewLearning Objectives
1.1 Deep Learning: The Series Introduction
03:461.2 What is a Neural Network
06:281.3 Three Reasons to go Deep
03:561.4 Your choice of Deep Net
02:581.5 An Old Problem
05:23
Lesson 3 - Deep Learning Models
22:56PreviewLearning Objectives
2.1 Restricted Boltzmann Machines
04:502.2 Deep Belief Nets
04:312.3 Convolutional Nets
08:152.4 Recurrent Nets
05:20
Lesson 4 - Additional Deep Learning Models
19:13Learning Objectives
3.1 Autoencoders
03:513.2 Recursive Neural Tensor Nets
05:493.3 Use Cases
09:33
Lesson 5 - Deep Learning Platforms & Libraries
25:37PreviewLearning Objectives
4.1 What is a Deep Net Platform?
03:414.2 H2O.ai
03:424.3 Dato GraphLab
03:324.4 What is a Deep Learning Library?
01:584.5 Theano
03:214.6 Caffe
02:484.7 Tensorflow
06:35Unlocking IBM Certificate
Project
Deep Learning Exam & Certification
What do I need to do to unlock my Simplilearn certificate?
To obtain the Deep Learning with TensorFlow certification, you will need to:- Attend one complete batch of Deep Learning course with Tensorflow training
- 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 course with Tensorflow training, you will be awarded an industry-recognized course completion certificate from Simplilearn which has 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
Deep Learning Course Reviews
A.Anthony Davis
KingstonThe 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
BangaloreGood 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., KolkataSimplilearn 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.
Why Simplilearn
Simplilearn’s Blended Learning model brings classroom learningexperience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience
Tensorflow Training FAQs
Why Learn Deep Learning with Tensorflow?
There is a growing adoption of Artificial Intelligence in customer-centric services and is expected to drive the market significantly. Deep Learning is a promising subset of Artificial Intelligence and Machine Learning technologies. The deep learning market is estimated to be valued at USD 3.18 Billion and is expected to be worth USD 18.16 billion by 2023.
Many tech giants are investing hugely in AI and currently offer attractive salary packages to the professionals skilled in Deep Learning. The entry-level professionals in this field earn an average salary of $95,000. With the rising demand, the field of deep learning is actively being explored by many professionals to add on to their existing skillset.
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 in 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 are the prerequisites for learning Deep Learning with Tensorflow?
Participants in this Deep Learning online course should have:
- Familiarity with programming fundamentals
- Fair understanding of basics of statistics and mathematics
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.
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