Instructor Led On-site Classes

USD 13,500.00
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Instructor

Instructor:

Artificial Intelligence/Deep Learning

Course Description

We will cover Artificial Intelligence/Deep Learning in depth through theory, TensorFlow labs, and consulting projects. The last four weeks will consist of hands-on projects where the students will have access to exclusive paid projects from real companies.

What am I going to get from this course?

Students will be able to apply Deep Learning methods in the field of Artificial Intelligence.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Candidates without a programming background will need to take our Python and Machine Learning Courses. Email us for more details [email protected]

Who should take this course? Who should not?

Candidates should take this course if they have a background in Data Science and Machine Learning in Python and are comfortable in statistics and/or mathematics.

Curriculum

Module 1: Week 1

Lecture 1 Fundamentals

Deep Learning in a Nutshell, Python, Notebooks, SciPy, NumPy, Pandas, Scikit-learn, Julia, Libraries, Cloud computing, Databases – SQL/NoSQL, Statistics: Frequentist vs Bayesian, Distributions, Algorithms,

Module 2: Week 2

Lecture 2 Machine Learning

Supervised Learning: Decision Trees, Naïve Bayesian Classifier, k-Nearest Neighbor, Support Vector Machines, Unsupervised Learning: Clustering, PCA, TensorFlow Labs

Module 3: Week 3

Lecture 3 Convolutional neural networks I

Computer Vision Overview, GPU computing, Convolutional neural networks, Frameworks, TensorFlow, Image classification, Autonomous vehicles, TensorFlow Labs

Module 4: Week 4

Lecture 4 Convolutional neural networks II

Second order methods, Visualizing CNNs, VGGNet examples, Use cases, TensorFlow Labs

Module 5: Week 5

Lecture 5 Recurrent neural networks I

Recurrent neural networks, LSTM, Natural language processing, Time series data, Image captioning, Video processing, TensorFlow Labs

Module 6: Week 6

Lecture 6 Recurrent neural networks II

IoT Overview, Sensor data, Hardware, Streaming data, Stream processing, Spark and Flink, Deep Learning Analysis, TensorFlow Labs

Module 7: Week 7

Lecture 7 Reinforcement Learning

Reinforcement learning, Deep Q Networks, Gaussian Processes, TensorFlow Labs

Module 8: Week 8

Lecture 8 Latest Developments

Bayesian Inference, Neural Turing Machine, Biocomputing, GAN, WaveNet, Connection with physical law, Neuromorphic computing, Business Reporting, Wrap up