Deep Learning Course with Hands-on Training

Become a Deep Learning expert by working on real-life case studies

  • 40 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks
  • Gain knowledge in Computer Vision applications
  • Covers TensorFlow and Keras for Deep Learning applications

Description

Deep learning is fast becoming among the most popular trends to be embraced by high profile companies.  Powered by big data, Deep Learning has made business more viable across healthcare, genomics, cybersecurity, e-commerce, agriculture and other sectors.

KnowledgeHut brings you a comprehensive course that will help you understand Deep learning and use it to generate business value. The workshop will help you learn the foundations of Deep Learning and understand how to build neural networks. You will also learn about Adam, Dropout, BatchNorm, Convolutional networks, RNNs, LSTM, and more. You will work on real life case studies to get hands-on experience. You will master not only the theory, but also see how it is applied in industry by learning to build models using Keras and Tensorflow.

A career in Deep Learning is much sought after because of the billions of dollars being spent on it and the need for Deep Learning experts. This workshop will help you gain the technical expertise for this technology and land lucrative positions.

What You Will Learn

Prerequisites
  • We recommend applicants to have knowledge of programming (preferably in Python)
  • Familiarity with statistics, algebra, probability and exposure to data analysis is preferred.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those interested in the field of deep learning and its applications.
  • Those wanting to use Deep learning techniques for effective analysis of large datasets
  • Software or Data Engineers interested in 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:

Learn about the basics on which Deep Learning has been constructed

Topics Covered:

  • Loss function
  • Cross entropy
  • K-nearest neighbour algorithm
  • Minimizing the error - Regression problem

Hands-on: No hands-on

Learning Objectives:

Learn the basics of neural networks and understand the biological inspiration behind the same. Learn to use vectorization to speed up your models. Learn to build a neural network with one hidden layer, using forward propagation and backpropagation. Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.Hands-on session on a real-life case study.

Topics Covered:

  • What is Neural Network?
  • The Biological Inspiration
  • Multilayer Perceptrons
  • Gradient Descent
  • Vectorization
  • Shallow Neural Networks
  • Activation Functions
  • Back Propagation Algorithm
  • Deep L-layer neural network
  • Forward Propagation in a Deep Network
  • Case Study: Neural Networks

Hands-on:

The dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph and compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. Can you develop a model that can beat the market and allow you to make statistically informed trades? Using Base Neural Network and Neural Network with Hidden layers, Activation function, Solver and Learning Rate , predict close value of stock.

Learning Objectives: 

Understand industry best-practices for building deep learning applications. Learn to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking. Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.Learn Keras for Classification and Regression in Typical Data Science Problems. Learn about  different layers in KERAS and set it up. Hands-on session on a real-life case study.

Topics Covered:

  • Hyperparameters tuning
  • Batch Normalization
  • Optimization algorithms
  • Deep Learning frameworks
  • Weight initialization
  • Deep Learning architecture
  • Introducing Keras
  • Artificial Neural Networks (ANN)
  • Case Study: Artificial Neural Networks (ANN)

Hands-on:

Apply Deep Learning framework - Keras to create a Neural Network, train models and monitor the same.Project research will be aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default is proven to be more valuable than the binary result of classification - credible or not credible clients.

Learning Objectives:

Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.

Topics Covered:

  • Convolutional Neural Networks (CNN)
  • Building blocks of CNN
  • Image Processing using CNN
  • Pre processing and semantic segmentation
  • Object localization and detection
  • Introducing Tensorflow
  • Case Study: Convolutional Neural Networks (CNN) using TensorFlow

Hands-on:  No Hands-on

Learning Objectives:

Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.

Topics Covered:

  • Object localization
  • Object detection 
  • Feature Extraction

Hands-on:  No Hands-on

Learning Objectives:

Get introduced to TensorFlow, a library. Learn to build a Neural Networks using Tensorflow. Hands-on session on a real-life case study

Topics Covered:

  • Introducing Tensorflow
  • Case Study: Convolutional Neural Networks (CNN) using TensorFlow

Hands-on:

Apply Deep Learning framework - TensorFlow to create a Neural Network and train models and monitor the same. Work on a project involving handwriting digit recognition using CNN with TensorFlow. This project will help build a model using Convolutional Neural Networks to recognize handwriting.

Learning Objectives:

Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. Hands-on session on a real-life case study.

Topics Covered:

  • Recurrent Neural Networks (RNN)
  • Backpropagation through time
  • Different types of RNNs
  • Language model and sequence generation
  • Gated Recurrent Unit (GRU)
  • Long Short Term Memory (LSTM)
  • Bidirectional RNN
  • Deep RNNs
  • Case Study: Recurrent Neural Networks (RNN)

Hands-on:

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:

Learn to use word vector representations and embed layers to train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation. Hands-on session on a real life case study.

Topics Covered:

  • Syntax and Parsing Techniques
  • Statistical NLP and text similarities
  • Text summarization techniques
  • Real-Life Case Study

Hands-on:

Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter. With proper tools and the help of technology, meaningful and precious information can be gathered, analyzed, and utilized in different areas like in the movement and performance of the stock market.

Projects

Predict close value of stock

The dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics

Read More

Classify credible or not credible clients using ANN

The research aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable

Read More

Handwriting digit recognition using CNN

Handwriting digit recognition using CNN with TensorFlow. This project will help build a model using Convolutional Neural Network to recognize handwriting

Using NLP, find an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter

Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions found its way in different social networking platforms

Read More
Note:These were the projects undertaken by students from previous batches.  

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FAQs

The Course

Deep learning has now found uses in every sector to make customer experience better and improve the quality of life. From translation to language recognition and autonomous vehicles to text generation, there are many uses of Deep Learning. Google, Apple, and Toyota are just some of the companies that have spent billions of dollars in developing Deep Learning research and products.

This trend has made deep learning enthusiasts among the most sought after professionals and this is a good workshop for you to master these skills and become proficient in deep learning concepts. The 5th-annual Burtch Works Study: Salaries of Data Scientists puts median compensations for individual contributors in a range of $95,000 at level 1 (0-3 years of experience) to $165,000 at level 3 (9+ years). Managers can earn $145,000 at level 1 (1-3 reports) to $250,000 at level 3 (10+ reports). So, this is the right time to invest in a career in Deep Learning.

You will gain these skills:Learn about Neural Networks, Convolutional Neural Networks, Recurrent Neural NetworksBe proficient in using TensorFlow and Keras
Get an understanding of Computer Vision applications
Get to know about libraries in Python used in Deep Learning

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.

Tools and Technology used are

  • 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 course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Deep Learning 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% deduction 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 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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