Course Outline
TensorFlow Basics
- Creation, Initializing, Saving, and Restoring TensorFlow variables
- Feeding, Reading and Preloading TensorFlow Data
- How to use TensorFlow infrastructure to train models at scale
- Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics
- Inputs and Placeholders
- Build the GraphS
- Inference
- Loss
- Training
- Train the Model
- The Graph
- The Session
- Train Loop
- Evaluate the Model
- Build the Eval Graph
- Eval Output
The Perceptron
- Activation functions
- The perceptron learning algorithm
- Binary classification with the perceptron
- Document classification with the perceptron
- Limitations of the perceptron
From the Perceptron to Support Vector Machines
- Kernels and the kernel trick
- Maximum margin classification and support vectors
Artificial Neural Networks
- Nonlinear decision boundaries
- Feedforward and feedback artificial neural networks
- Multilayer perceptrons
- Minimizing the cost function
- Forward propagation
- Back propagation
- Improving the way neural networks learn
Convolutional Neural Networks
- Goals
- Model Architecture
- Principles
- Code Organization
- Launching and Training the Model
- Evaluating a Model
Requirements
Background in physics, mathematics and programming. Involvment in image processing activities.
Testimonials
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
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Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
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Really simple, easy to follow explanations Covered everything necessary in enough detail to understand fully, but so that it was not overwhelming good mix of theory and practice
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
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Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
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That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
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The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
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The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
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Graphs in R :)))
Faculty of Economics and Business Zagreb
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We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
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new insights in deep machine learning
Josip Arneric
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the interactive part, tailored to our specific needs
Thomas Stocker
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Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
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It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.