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TensorFlow Training

Our CEO, Chander Dhall, became fascinated with machine learning over a decade ago. Having a masters in computer science, he has always kept up with academia even though the company primarily works on projects for mid and large size Fortune 500 corporations. Having been awarded by both Microsoft (Microsoft Most Valuable Professional for eight straight years) and Google (Google Developer Expert), he has been fortunate to interact with and share knowledge with the ones who create these technologies.

Since Cazton is a big supporter of open source, we have experts who have contributed to open source machine learning libraries as well as data engineering libraries. Our team consists of experts who not only have PhDs as well as Masters’ degrees in data science and machine learning and are Open Source contributors who have created their own ML libraries, they also have years of experience in the industry. That’s one reason our team has been working on serious machine learning projects long before any of our competitors. Machine learning projects require serious understanding of data. Cazton data scientists with years of successful experience in the industry have worked in multiple business domains. Our machine learning experts not only evaluate the data, but are adept at all facets of data engineering. If you are serious about doing machine learning right, please check out our expert machine learning team of PhDs, as well as Microsoft-awarded Most Valuable Professionals and Google Developer Experts.

The great news is that these experts do the training for you. We are also one of the very few companies that offer flexible trainings. For example, if you want to learn machine learning, but also want to learn data engineering and data science we can do that for you. If you want to learn TensorFlow, but also want to learn Scala, Hadoop, Ignite, Kafka and related technologies we can do that for you. Some examples include Numpy, Keras, Scipy, Sci-kit, Theano, Torch, Caffe, etc.

Below is a sample curriculum, which can be modified as needed to suit your needs. Contact us today to learn more.

Understanding Machine Learning

  • Introduction
  • Historical developments
  • Practical Uses
  • Machine Learning vs Artificial Intelligence
  • Machine Learning vs Deep Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Machine Learning Actions

  • Classification
  • Regression
  • Clustering
  • Density Estimation
  • Reduction of dimensionality

Machine Learning Algorithms

  • Linear Regression
  • Naïve Bayes
  • Random Tree

Neural Networks

  • Introduction to Neural Networks
  • Classical architecture
  • Activation functions
  • The perceptron learning algorithm
  • Binary classification with the perceptron
  • Document classification with the perceptron
  • Limitations of the perceptron
  • Understanding bias
  • Depth of a network
  • Back propagation

Neural Networks (Deep Dive)

  • Stochastic Gradient Descent
  • Modeling of a neural network
  • Approximation of a function
  • Approximation of a distribution
  • Data Augmentation
  • Result Generalization
  • Regularization
  • Optimization and Convergence algorithms.

Introduction to TensorFlow

  • Introduction to vectors
  • Variables (create, initialize, save and restore)
  • Data (feed, read and preload)
  • Training and Test data
  • Models
  • Optimizing models

Convolutional Neural Networks (CNN)

  • Introduction
  • Kernel
  • Feature Map Generation
  • CNN architectures
  • Attention Model
  • Application of a CNN
  • CNNs for generation
  • Feature maps for image generation

Recurrent Neural Networks (RNN)

  • Introduction
  • Basic operation of the RNN
  • Gated Recurrent Units (GRUs)
  • LSTM (Long Short-Term Memory)
  • Convergence
  • Vanishing gradient problems
  • Prediction of a temporal series
  • RNN Encoder Decoder
  • NLP applications
  • Video Applications

Deep Reinforcement Learning

  • Introduction
  • Deep Q Learning
  • Optimization of learning policy
  • Application of Deep Reinforcement Learning

Artificial Neural Networks (ANN)

  • Nonlinear decision boundaries
  • Feedforward ANNs
  • Feedback ANNs
  • Multilayer Perceptron
  • Forward Propagation
  • Back Propagation
  • Minimizing the cost function
  • Improving the neural networks

Tensorflow - Advanced

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing your Model
  • Customizing Data Readers
  • Manipulating TensorFlow Model Files

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial

Cazton has TensorFlow Consultants and Trainers who can provide expert guidance and training as per your organization’s needs. Over the years, Cazton has expanded into a global company servicing client’s not only across the United States, but in Europe and Canada as well. In the United States, we provide our TensorFlow Consulting and Training Services across various cities like Austin, Dallas, Houston, New York, New Jersey, Irvine, Los Angeles, Denver, Boulder, Charlotte, Atlanta, Orlando, Miami, San Antonio, San Diego and others. Our Experts remain committed to the vision of transforming our clients and their team by providing the best training experience. Contact us today to learn more about what our experts can do for you.

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