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Home » Technology » AI / Machine Learning » TensorFlow Machine Learning Cookbook

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    TFMLCBL21E09

Who should attend & recommended skills:

Developers with basic Python and machine learning experience

Who should attend & recommended skills

  • Developers who want to skip the theory and get the most out of Tensorflow to build production-ready machine learning models.
  • Skill-level: Foundation-level TensorFlow Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class.
  • Machine learning: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience)

About this course

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this course will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this course, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google’s machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the course, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.

Skills acquired & topics covered

  • Exploiting the features of Tensorflow to build and deploying machine learning models
  • Training neural networks to tackle real-world problems in Computer Vision and NLP
  • Techniques to write production-ready code for your Tensorflow models
  • The basic features of the TensorFlow library
  • Linear Regression techniques with TensorFlow
  • SVMs with hands-on recipes
  • Implementing neural networks to improve predictive modeling
  • Applying NLP and sentiment analysis to your data
  • Mastering CNN and RNN through practical recipes
  • Implementing the gradient boosted random forest to predict housing prices
  • Taking TensorFlow into production

Course breakdown / modules

  • Introduction
  • How TensorFlow works
  • Declaring variables and tensors
  • Using placeholders and variables
  • Working with matrices
  • Declaring operations
  • Implementing activation functions
  • Working with data sources
  • Additional resources

  • Introduction
  • Operations in a computational graph
  • Layering nested operations
  • Working with multiple layers
  • Implementing loss functions
  • Implementing backpropagation
  • Working with batch and stochastic training
  • Combining everything together
  • Evaluating models

  • Introduction
  • Using the matrix inverse method
  • Implementing a decomposition method
  • Learning the TensorFlow way of linear regression
  • Understanding loss functions in linear regression
  • Implementing deming regression
  • Implementing lasso and ridge regression
  • Implementing elastic net regression
  • Implementing logistic regression

  • Introduction
  • Working with a linear SVM
  • Reduction to linear regression
  • Working with kernels in TensorFlow
  • Implementing a non-linear SVM
  • Implementing a multi-class SVM

  • Introduction
  • Working with nearest-neighbors
  • Working with text based distances
  • Computing with mixed distance functions
  • Using an address matching example
  • Using nearest-neighbors for image recognition

  • Introduction
  • Implementing operational gates
  • Working with gates and activation functions
  • Implementing a one-layer neural network
  • Implementing different layers
  • Using a multilayer neural network
  • Improving the predictions of linear models
  • Learning to play Tic Tac Toe

  • Introduction
  • Working with bag-of-words embeddings
  • Implementing TF-IDF
  • Working with Skip-Gram embeddings
  • Working with CBOW embeddings
  • Making predictions with word2vec
  • Using doc2vec for sentiment analysis

  • Introduction
  • Implementing a simple CNN
  • Implementing an advanced CNN
  • Retraining existing CNN models
  • Applying stylenet and the neural-style project
  • Implementing DeepDream

  • Introduction
  • Implementing RNN for spam prediction
  • Implementing an LSTM model
  • Stacking multiple LSTM layers
  • Creating sequence-to-sequence models
  • Training a Siamese similarity measure

  • Introduction
  • Implementing unit tests
  • Using multiple executors
  • Parallelizing TensorFlow
  • Taking TensorFlow to production
  • An example of productionalizing TensorFlow
  • Using TensorFlow Serving

  • Introduction
  • Visualizing graphs in TensorBoard
  • Working with a genetic algorithm
  • Clustering using k-means
  • Solving a system of ordinary differential equations
  • Using a random forest
  • Using TensorFlow with Keras

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