Number of sessions:Â 2 Days
Delivery: Online through QuAcademy with Video, Demos, Case studies + Labs
Deep learning is a technique for automatically finding hierarchical patterns in large sets of data, and has been used to achieve breakthrough advances in computer vision, machine translation, speech recognition, game playing, robotics, and other applications in recent years. The recent progress and future potential of deep learning has led to immense interest and to its adoption by all large technology companies.
 In this workshop, we’ll introduce deep learning and demonstrate how it can be used in the above areas, with a focus on practical applications. We will explain the technological and algorithmic advances that have made it possible, describe the tools you can use to get started, and talk about the challenges to deploying deep learning systems in production.
What you will learn
In this workshop, you will learn the core techniques used in Deep Learning. Through examples in Keras, TensorFlow and Apache Spark, you will learn
- Basics of Neural Networks
- Core Deep Learning Techniques including CNNs, RNNs, AutoEncoders
- Limitations and challenges of using deep neural networks.
- Fine tuning and considerations when working with Deep Neural Networks
- Deep Learning and Apache Spark
Practical case studies with fully functional code
After this workshop, you will be able to:
- Describe what deep neural networks (DNNs) are, what they can be used for, and how they fit with other AI techniques.
- Explain what computer games have to do with the success of deep learning.
- Describe several deep learning technologies and their tradeoffs.
- Explain the limitations and challenges of using DNNs.
- Train and tune simple DNNs and evaluate their performance, using Python and several deep learning frameworks.
- Describe how DNNs are likely to affect your field in the next 5-10 years.
- Work on case studies in Keras,TensorFlow and Apache Spark
On day one, we will review the core techniques in Deep learning neural networks. Through examples we will understand the different deep learning techniques and frameworks
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- Introduction to deep neural networks
- Hands on with Keras and TensorFlow
- Intro to IBM Data Science Experience
- Convolutional neural networks
- Recurrent neural networks for translation, sentiment detection, and other text applications
- Case study 1: Classifying images using fully connected neural networks and convolutional networks
- Case study 2: Monitoring network learning and status using ad-hoc plots and TensorBoard
- Case study 3: Comparing performance of GPU and CPU network training
- Case study 4: Using pre-trained models for identifying objects in photos.
On day two, we will discuss advanced techniques in anomaly detection and use Apache Spark for anomaly detection. We will also discuss best practices in scaling and using anomaly detection techniques.
- Anomaly Detection: Advanced techniques
- Looking for anomolies in large and complex data sets
- Apache Spark: A brief introduction
- Anomaly Detection and Fraud Detection in Temporal datasets
- Case study 3: Using Apache Spark for Anomaly detection
- Deep Learning techniques in Anomaly detection
- Case study 4: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
Course instructor:
Sri Krishnamurthy, CFA
Chief Data Scientist, QuantUniversity
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Sri Krishnamurthy is the founder of www.quantuniversity.com, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than two decades of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications.
Prior to starting QuantUniversity, Sri has worked at Citigroup, Endeca, MathWorks and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School.
Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.
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QuantUniversity (www.quantuniversity.com) is a quantitative analytics and machine learning advisory based in Boston, Massachusetts. QuantUniversity runs various data science and machine learning workshops in Boston, New York, Chicago, San Francisco and online. The company offers an Analytics Certificate Program and the Fintech Certificate program along with multiple workshops in its Explore-Experience-Excel series. Contact us at info@qusandbox.com
Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..