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TensorFlow Machine Learning Cookbook

  • Course Code: Data Science - TensorFlow Machine Learning Cookbook
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Skip the theory and get the most out of Tensorflow to build production-ready machine learning models.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level TensorFlow Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Skip the theory and get the most out of Tensorflow to build production-ready machine learning models. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our TensorFlow Machine Learning Cookbook expert instructor, students will learn about and explore: 

  • Exploit the features of Tensorflow to build and deploy machine learning models 
  • Train neural networks to tackle real-world problems in Computer Vision and NLP 
  • Handy techniques to write production-ready code for your Tensorflow models 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Become familiar with the basic features of the TensorFlow library 
  • Get to know Linear Regression techniques with TensorFlow 
  • Learn SVMs with hands-on recipes 
  • Implement neural networks to improve predictive modeling 
  • Apply NLP and sentiment analysis to your data 
  • Master CNN and RNN through practical recipes 
  • Implement the gradient boosted random forest to predict housing prices 
  • Take TensorFlow into production 

Audience & Pre-Requisites 

This course is designed for developers wants to Skip the theory and get the most out of Tensorflow to build production-ready machine learning models. 

Pre-Requisites:  Students should have familiar with  

  • Basics of ML  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Getting Started with TensorFlow 
  • Getting Started with TensorFlow 
  • 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 
  1. The TensorFlow Way 
  • The TensorFlow Way 
  • 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 
  1. Linear Regression 
  • Linear Regression 
  • 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 
  1. Support Vector Machines 
  • Support Vector Machines 
  • 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 
  1. Nearest-Neighbor Methods 
  • Nearest-Neighbor Methods 
  • 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 
  1. Neural Networks 
  • Neural Networks 
  • 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 
  1. Natural Language Processing 
  • Natural Language Processing 
  • 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 
  1. Convolutional Neural Networks 
  • Convolutional Neural Networks 
  • Introduction 
  • Implementing a simple CNN 
  • Implementing an advanced CNN 
  • Retraining existing CNN models 
  • Applying stylenet and the neural-style project 
  • Implementing DeepDream 
  1. Recurrent Neural Networks 
  • Recurrent Neural Networks 
  • Introduction 
  • Implementing RNN for spam prediction 
  • Implementing an LSTM model 
  • Stacking multiple LSTM layers 
  • Creating sequence-to-sequence models 
  • Training a Siamese similarity measure 
  1. Taking TensorFlow to Production 
  • Taking TensorFlow to Production 
  • Introduction 
  • Implementing unit tests 
  • Using multiple executors 
  • Parallelizing TensorFlow 
  • Taking TensorFlow to production 
  • An example of productionalizing TensorFlow 
  • Using TensorFlow Serving 
  1. More with TensorFlow 
  • More with TensorFlow 
  • 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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