100% Money Back Guarantee Description
Big data and data science are the careers of the future. Powered by big data, Deep Learning has made business more viable across healthcare, genomics, cybersecurity, e-commerce, agriculture, and other sectors and this is the right time to invest in a career in Deep Learning. Deep-learning networks are distinguished from ordinary neural networks by having more hidden layers, or so-called more depth.
Deep learning engineers who are experts in libraries such as TensorFlow are in much demand for their ability to implement deep learning for numerical computation of mathematical expressions, using data flow graphs. Developed in the Google labs, TensorFlow is one of the best libraries to implement advanced techniques in deep learning. This workshop will help you understand TensorFlow and explore deep neural networks and layers of data abstraction. Through this hands-on workshop, you will gain real-world contextualization through deep learning problems concerning research and application.
What You Will Learn
1. Understand TensorFlow
Apply deep machine intelligence and GPU computing with TensorFlow
2. Application of TensorFlow in real life
Access public datasets and use TensorFlow to load, process, and transform the data
3. Building models using TensorFlow
Discover how to use the high-level TensorFlow API to build more powerful applications
4. Detection of Object
Use deep learning for scalable object detection and mobile computing
5. Recommendation Systems Using Factorization Machines
Explore active areas of deep learning research and applications
Prerequisites
- We recommend applicants to have knowledge of programming (preferably in Python).
- Familiarity with statistics, algebra, probability, and exposure to data science is preferred.
Who should Attend?
- Those interested in computer vision, image restoration, text classification etc.
- Those working with large datasets who want to simplify the effective analysis
- Software or Data Engineers interested in learning about Deep Learning
KnowledgeHut Experience
Instructor-led Live Classroom
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Curriculum Designed by Experts
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn through Doing
Learn theory backed by practical case studies, exercises, and coding practice. Get skills and knowledge that can be effectively applied.
Mentored by Industry Leaders
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Advance from the Basics
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Code Reviews by Professionals
Get reviews and feedback on your final projects from professional developers.
Curriculum
Learning Objectives:
In this module, you will learn the basic concepts of Machine Learning (ML) and Deep Learning (DL). You will start off with a brief introduction to ML and then move on to DL, which is a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data.
Topics:
- A soft introduction to ML
- Artificial neural networks
- ML versus DL
- DL neural network architectures
- Available DL frameworks
Hands-on: No hands-on
Learning Objectives:
This module will help you learn and write Python code using Deep Learning framework - TensorFlow. You will learn to use TensorFlow to visualize computations through Tensorboards.
Topics:
- TensorFlow computational graph
- TensorFlow code structure
- TensorFlow data model
- Visualizing computations through TensorBoard
- Linear regression and beyond
Hands-on:
Write Python code using Deep Learning framework - Use TensorFlow to visualize computations through Tensorboards.
Learning Objectives:
This module will teach you to implement a layered neural network. You will also learn about hyperparameter tuning and dropout optimization in an FFNN.
Topics:
- Implementing a feedforward network
- Implementing a multilayer perceptron
- Tuning hyperparameters and advanced FFNN
Hands-on:
Implement a layered Neural Network using TensorFlow.
Learning Objectives:
Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on objects that appear in them. Also understand how to use these networks to learn data compression and image denoising and learn about CNN using TensorFLow through a real Life Case Study.
Topics:
- Main concepts in CNN
- CNN in action
- Fine tuning implementation
- Case Study on CNN
Hands-on:
Handwriting digit recognition using CNN with TensorFlow. This project will help build a model using Convolutional Neural Networks to recognize handwriting.
Design and train convolutional neural network models to classify images using TensorFlow & Keras.
Learning Objectives:
In this module you will learn how an autoencoder works and implement the same. You will also learn to improve the robustness of autoencoder.In this module you will learn how an autoencoder works and implement the same. You will also learn to improve the robustness of autoencoder.
Topics:
- How does an autoencoder work?
- How to implement an autoencoder
- Improving autoencoder robustness
- Building denoising autoencoders
- Convolutional autoencoders
Hands-on : No Hands-on
Learning Objectives:
Build your own recurrent networks and long short-term memory networks with Keras and TensorFlow; perform sentiment analysis and generate new text. Learn RNN using TensorFLow with a real Life Case Study.
Topics:
- Working principles of RNNs
- RNNs and the gradient vanishing-exploding problem
- LSTM networks
- Implementing an RNN
- Case Study on RNN
Hands-on: Implement RNN using Keras.
A time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Using Long-Short-Term-Memory (LSTM) build a time series model to forecast the future values.
Learning Objectives:
You will Explore the fundamental topic on TensorFlow considering the possibility of executing TensorFlow models on GPU cards and distributed systems.
Topics:
- GPU computing
- The TensorFlow GPU setup
- Distributed computing
- The distributed TensorFlow setup
Hands-on:
Hands-on practice in setting up a TensorFlow GPU.
Learning Objectives:
In this module you will learn about the theoretical background of recommendation systems, such as matrix factorization, about UBCF and how is it used in Recommender Engines. You will also learn concepts like cold-start problems, about IBCF and how it is used in Recommender Engines. The module covers the use of Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems. You will also study about Recommender Systems with a real Life Case Study.
Topics:
- Recommendation systems
- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- FM-based recommendation systems
- Case Study on Recommender Systems
Hands-on:
You do not need a market research team to know what your customers are willing to buy. Netflix successfully used recommender system to recommend movies to its viewers. As estimated by Netflix, its recommendation engine is worth nearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build a Recommender System for a Retail Chain to recommend the right products to its users.
Projects
Handwriting digit recognition using CNN with TensorFlow.
This project will help build a model using Convolutional Neural Network to recognize handwriting. Design and train convolutional neural network models to classify images using TensorFlow &
Build a time series model to forecast future values using LSTM
A time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Using Long-Short-Term-Memory (LSTM) build a time series model to forecast the future
Build Recommender System for a Retail Chain to recommend the right products to its users
You do not need a market research team to know what your customers is willing to buy. Netflix is a big example, having successfully used recommender system to recommend movies to its viewers. Netflix
FAQs
The Course
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:
- Get to know about TensorFlow Framework
- Learn about Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks using TensorFlow
- Be proficient in using TensorFlow
- Get an understanding of Computer Vision applications using TensorFlow
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.
- Python
- TensorFlow
- Keras
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.
Finance Related
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for a refund. Kindly go through our Refund Policy for more details.
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 Refund Policy.
The Remote Experience
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
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