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Data Sciences

TensorFlow Machine Learning Cookbook

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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.

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Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

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.

Course Curriculum

    • Getting Started with TensorFlow 00:00:00
    • Introduction 00:00:00
    • How TensorFlow works 00:00:00
    • Declaring variables and tensors 00:00:00
    • Using placeholders and variables 00:00:00
    • Working with matrices 00:00:00
    • Declaring operations 00:00:00
    • Implementing activation functions 00:00:00
    • Working with data sources 00:00:00
    • Additional resources 00:00:00
    • The TensorFlow Way 00:00:00
    • Introduction 00:00:00
    • Operations in a computational graph 00:00:00
    • Layering nested operations 00:00:00
    • Working with multiple layers 00:00:00
    • Implementing loss functions 00:00:00
    • Implementing backpropagation 00:00:00
    • Working with batch and stochastic training 00:00:00
    • Combining everything together 00:00:00
    • Evaluating models 00:00:00
    • Linear Regression 00:00:00
    • Introduction 00:00:00
    • Using the matrix inverse method 00:00:00
    • Implementing a decomposition method 00:00:00
    • Learning the TensorFlow way of linear regression 00:00:00
    • Understanding loss functions in linear regression 00:00:00
    • Implementing deming regression 00:00:00
    • Implementing lasso and ridge regression 00:00:00
    • Implementing elastic net regression 00:00:00
    • Implementing logistic regression 00:00:00
    • Support Vector Machines 00:00:00
    • Introduction 00:00:00
    • Working with a linear SVM 00:00:00
    • Reduction to linear regression 00:00:00
    • Working with kernels in TensorFlow 00:00:00
    • Implementing a non-linear SVM 00:00:00
    • Implementing a multi-class SVM 00:00:00
    • Nearest-Neighbor Methods 00:00:00
    • Introduction 00:00:00
    • Working with nearest-neighbors 00:00:00
    • Working with text based distances 00:00:00
    • Computing with mixed distance functions 00:00:00
    • Using an address matching example 00:00:00
    • Using nearest-neighbors for image recognition 00:00:00
    • Neural Networks 00:00:00
    • Introduction 00:00:00
    • Implementing operational gates 00:00:00
    • Working with gates and activation functions 00:00:00
    • Implementing a one-layer neural network 00:00:00
    • Implementing different layers 00:00:00
    • Using a multilayer neural network 00:00:00
    • Improving the predictions of linear models 00:00:00
    • Learning to play Tic Tac Toe 00:00:00
    • Natural Language Processing 00:00:00
    • Introduction 00:00:00
    • Working with bag-of-words embeddings 00:00:00
    • Implementing TF-IDF 00:00:00
    • Working with Skip-Gram embeddings 00:00:00
    • Working with CBOW embeddings 00:00:00
    • Making predictions with word2vec 00:00:00
    • Using doc2vec for sentiment analysis 00:00:00
    • Convolutional Neural Networks 00:00:00
    • Introduction 00:00:00
    • Implementing a simple CNN 00:00:00
    • Implementing an advanced CNN 00:00:00
    • Retraining existing CNN models 00:00:00
    • Applying stylenet and the neural-style project 00:00:00
    • Implementing DeepDream 00:00:00
    • Recurrent Neural Networks 00:00:00
    • Introduction 00:00:00
    • Implementing RNN for spam prediction 00:00:00
    • Implementing an LSTM model 00:00:00
    • Stacking multiple LSTM layers 00:00:00
    • Creating sequence-to-sequence models 00:00:00
    • Training a Siamese similarity measure 00:00:00
    • Taking TensorFlow to Production 00:00:00
    • Introduction 00:00:00
    • Implementing unit tests 00:00:00
    • Using multiple executors 00:00:00
    • Parallelizing TensorFlow 00:00:00
    • Taking TensorFlow to production 00:00:00
    • An example of productionalizing TensorFlow 00:00:00
    • Using TensorFlow Serving 00:00:00
    • More with TensorFlow 00:00:00
    • Introduction 00:00:00
    • Visualizing graphs in TensorBoard 00:00:00
    • Working with a genetic algorithm 00:00:00
    • Clustering using k-means 00:00:00
    • Solving a system of ordinary differential equations 00:00:00
    • Using a random forest 00:00:00
    • Using TensorFlow with Keras 00:00:00

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