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Course Machine Learning with TensorFlow

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  • Course Machine Learning with TensorFlow : Content

    In the course Machine Learning with TensorFlow participants learn to implement machine learning and deep learning applications with the open source TensorFlow framework. TensorFlow comes from Google and uses Python. With TensorFlow you can train and implement neural networks for number classification, image recognition and other problems.

    TensorFlow Machine Learning

    The course Machine Learning with TensorFlow starts with an overview of the basic principles of Machine Learning and an explanation of the differences of Supervised, Unsupervised and Deep Learning. The data types of TensorFlow like vectors, arrays, lists and scalars are treated and the Colab and DataBricks development environments are discussed.

    Tensors

    Subsequently the Machine Learning with TensorFlow course pays attention to the central Tensor Data Structure, which can be regarded as a container in which data in N dimensions can be stored. Rank, shape and type of tensors are discussed and TensorFlow operations and sessions are also treated.

    Neural Networks

    Special attention is given to neural networks in which both Convolutional and Recurrent Neural Networks are explained. Convolution and Pooling, making connections between Input Neurons and Hidden Layers are also discussed.

    Model Visualization

    The Visualization of models with TensorBoard is also part of the Machine Learning with TensorFlow course. Supervised Learning with Linear and Logistic Regression are reviewed and Ensemble techniques and Gradient Boosting are explained.

    Text Processing

    In addition the course Machine Learning with TensorFlow deals with Natural Language Processing with tokenization and text classification. Spam detection serves as an example and also Deep Learning is on the course schedule.

    TensorFlow Optimizers

    Various TensorFlow Optimizers such as Stochastic Gradient Descent, Gradient clipping and Momentum are discussed as well. And also Image Processing with Dimensionality Reduction and using the Keras APIs is covered.

    Model Deployment

    Finally the course Machine Learning with TensorFlow ends with a discussion of models in production. Models as REST Service and Keras Based Models are treated.

  • Course Machine Learning with TensorFlow : Training

    Audience Course Machine Learning with Tensor Flow

    The course Machine Learning with TensorFlow is intended for data scientists who want to use Python and the TensorFlow machine learning libraries to make predictions based on models.

    Prerequisites for course Machine Learning with TensorFlow

    To participate in this course knowledge of and experience with Python is required and knowledge of data analysis libraries such as Numpy, Pandas and Matplotlib is desirable.

    Realization training Machine Learning with TensorFlow

    The theory is discussed on the basis of presentations. Illustrative demos clarify the concepts. The theory is interchanged with exercises. The Anaconda distribution with Jupyter notebooks is used as a development environment. Course times are from 9:30 to 16:30.

    Official Certificate Machine Learning with TensorFlow

    After successful completion of the course participants receive an official certificate Machine Learning with TensorFlow.

    Course Machine Learning with TensorFlow
  • Course Machine Learning with TensorFlow : Modules

    Module 1 : Intro TensorFlow

    Module 2 : Tensor Data Structure

    Module 3 : Neural Networks

    What is TensorFlow?
    Machine Learning
    Supervised Learning
    Unsupervised Learning
    Deep Learning
    Install Anaconda
    Install TensorFlow
    Colab and Databricks
    Vectors and Scalars
    Matrix Calculations
    Arrays and Lists
    Multiple Dimensions
    Rank, Shape and Type
    TensorFlow Dimensions
    Tensor Manipulations
    TensorFlow Graphs
    Variables and Constants
    TensorFlow Operations
    TensorFlow Sessions
    Placeholders
    What are Neural Networks?
    Convolutional Neural Networks
    Multiple Layers of Arrays
    Local respective fields
    Convolution and Pooling
    Connecting Input Neurons
    Hidden Layers
    Recurrent Neural Networks
    Sequential Approach
    Layer Independence

    Module 4 : Tensor Board

    Module 5 : Supervised Learning

    Module 6 : Natural Language Processing

    Data Visualization
    Data Flow Graph
    High Level Blocks
    High Degree Nodes
    Node Representations
    Sequence Numbered Nodes
    Connected Nodes
    Operation Nodes
    Summary Nodes
    Reference Edge
    Linear Regression
    Keras and TensorFlow
    Correlation Graph
    Pairplot
    Logistic Regression
    Categorical Outcomes
    Sigmoid Function
    Boosted Trees
    Ensemble Technique
    Gradient Boosting
    NLP Overview
    NLP Curves
    Text Preprocessing
    Tokenization
    Spam Detection
    Word Embeddings
    Deep Learning Model
    Text Classification
    Text Processing
    TensorFlow Projector

    Module 7 : TensorFlow Optimizers

    Module 8 : Image Processing

    Module 9 : Models in Production

    Stochastic Gradient Descent
    Gradient clipping
    Momentum
    Nesterov momentum
    Adagrad
    Adadelta
    RMSProp
    Adam
    Adamax
    SMORMS3
    Convolution Layer
    Pooling Layer
    Fully Connected Layer
    Keras API's
    ConvNets
    Transfer Learning
    Autoencoders
    Dimensionality Reduction
    Compression Techniques
    Variational Autoencoders
    Model Deployment
    Isolation
    Collaboration
    Model Updates
    Model Performance
    Load Balancer
    Model as REST Service
    Templates
    Keras Based Models
    Flask Challenges
  • Course Machine Learning with TensorFlow : General

    Course Forms

    All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.

    Customization

    We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.

    Course times

    The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.

    Hardware

    We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.

    Software

    Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.

    Lunch

    The course includes lunch that we use in a restaurant within walking distance of the course room.

    Locations

    The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.

    Evaluations

    At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.

    Copyright

    The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.

  • Course Machine Learning with TensorFlow : Reviews

  • Course Machine Learning with TensorFlow : Certificate