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.
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,
Supervised Learning: Decision Trees, Naïve Bayesian Classifier, k-Nearest Neighbor, Support Vector Machines, Unsupervised Learning: Clustering, PCA, TensorFlow Labs
Computer Vision Overview, GPU computing, Convolutional neural networks, Frameworks, TensorFlow, Image classification, Autonomous vehicles, TensorFlow Labs
Second order methods, Visualizing CNNs, VGGNet examples, Use cases, TensorFlow Labs
Recurrent neural networks, LSTM, Natural language processing, Time series data, Image captioning, Video processing, TensorFlow Labs
IoT Overview, Sensor data, Hardware, Streaming data, Stream processing, Spark and Flink, Deep Learning Analysis, TensorFlow Labs
Reinforcement learning, Deep Q Networks, Gaussian Processes, TensorFlow Labs
Bayesian Inference, Neural Turing Machine, Biocomputing, GAN, WaveNet, Connection with physical law, Neuromorphic computing, Business Reporting, Wrap up
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