3 Months FREE Access to all our E-learning courses when you buy any course with us
Module 1- Getting started with TensorFlow
Learning Objectives: Learn the basic concepts of Machine Learning (ML) and Deep Learning (DL). We will start with a brief introduction to ML. Then we will move to DL, which is a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data.
Module 2- Overview of TensorFlow
Learning Objectives: Write Python code using Deep Learning framework - TensorFlow to visualize computations through Tensorboards
Module 3- Feed-forward neural networks with TensorFlow
Learning Objectives: Learn to implement a layered neural network also learn about hyperparameter tuning and dropout optimization in an FFNN
Module 4- Convolutional Neural Network (CNN)
Learning Objectives: Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on objects that appear in them. Use these networks to learn data compression and image denoising
Real Life Case Study on CNN using TensorFLow
Module 5- Optimizing TensorFlow Autoencoders
Learning Objectives: Learn how an autoencoder works and implement the same. Learn to improve the robustness of autoencoder
Module 6- Recurrent Neural Networks
Learning Objectives: Build your own recurrent networks and long short-term memory networks with Keras and TensorFlow; perform sentiment analysis and generate new text
Real Life Case Study on RNN using TensorFLow
Module 7- Heterogeneous and Distributed Computing
Learning Objectives: Explore the fundamental topic on TensorFlow considering the possibility of executing TensorFlow models on GPU cards and distributed systems
Module 8- Recommendation Systems Using Factorization Machines
Learning Objectives: Learn about the theoretical background of recommendation systems, such as matrix factorization
Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems
Learn what is IBCF and how is it used in Recommender Engines
Learn how to use Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems
Real Life Case Study with Recommender Systems
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users
Covers Artificial Neural Networks, Convolutional Neural Networks, Long Short Term Memory
Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.Attended workshop in April 2018
The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.Attended workshop in July 2018
Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.Attended workshop in May 2018
The training was very interactive and engaging with the attendees.Attended workshop in June 2018
Deep learning is among the hottest career trends in the market right now. According to Indeed.com the average salary for "deep learning" ranges from approximately $72,172 per year for Research Scientist to $146,075 per year for Computer Vision Engineer.
This course will help you master TensorFlow, one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs.
You will gain the following practical skills from this course:
By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as data analysts.
There are no restrictions but participants would benefit if they have Python programming knowledge and familiarity with Data Science.
Yes, KnowledgeHut offers this training online.
On successful completion of the Tensorflow course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Data Science experts who have years of industry experience.
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund
KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor