Building machine learning projects with tensorflow pdf download

You will master not only the theory, but also see how it is applied in industry. You've learned how to build and train models. Now learn to navigate various deployment scenarios and use data more effectively to train your model in this four-course Specialization. This specialization is for software and ML engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

To go deeper with your ML knowledge, these resources can help you understand the underlying math concepts necessary for higher level advancement. A bird's-eye view of linear algebra for machine learning. Never taken linear algebra or know a little about the basics, and want to get a feel for how it's used in ML? Then this video is for you.

This online specialization from Coursera aims to bridge the gap of mathematics and machine learning, getting you up to speed in the underlying mathematics to build an intuitive understanding, and relating it to Machine Learning and Data Science.

In this video series, you will learn the basics of a neural network and how it works through math concepts. A series of short, visual videos from 3blue1brown that explain the geometric understanding of matrices, determinants, eigen-stuffs and more. A series of short, visual videos from 3blue1brown that explain the fundamentals of calculus in a way that give you a strong understanding of the fundamental theorems, and not just how the equations work.

This introductory course from MIT covers matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices. This introductory calculus course from MIT covers differentiation and integration of functions of one variable, with applications. This book provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex world of datasets needed to train models in machine learning.

We've gathered our favorite resources to help you get started with TensorFlow libraries and frameworks specific to your needs.

Jump to our sections for TensorFlow. You can also browse the official TensorFlow guide and tutorials for the latest examples and colabs. Machine Learning Foundations is a free training course where you'll learn the fundamentals of building machine learned models using TensorFlow. This ML Tech Talk includes representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow.

In this series, the TensorFlow Team looks at various parts of TensorFlow from a coding perspective, with videos for use of TensorFlow's high-level APIs, natural language processing, neural structured learning, and more. Learn to spot the most common ML use cases including analyzing multimedia, building smart search, transforming data, and how to quickly build them into your app with user-friendly tools. Explore the latest resources at TensorFlow.

A 3-part series that explores both training and executing machine learned models with TensorFlow. Graphics in this book are printed in black and white.

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.

About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices.

Examples use the super-stable 1. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Gain real-world contextualization through some deep learning problems concerning research and application Book Description Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction.

Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.

This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.

Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications Who this book is for The book is for people interested in machine learning and machine intelligence.

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. The most popular method for classification is logistic regression.

Logistic regression is a probabilistic and linear classifier. The probability that the vector of input features belongs to a specific class can be described mathematically by the following equation:. Thus, the model has to be trained to maximize the value of this probability. The sigmoid function transforms the y value to be between the range [0,1]. The model training means to search for the parameters that minimize the loss function, which can either be the sum of squared errors or the sum of mean squared errors.

For logistic regression, the likelihood is maximized as follows:. However, as it is easier to maximize the log-likelihood, we use the log-likelihood l w as the cost function. The loss function J w is written as -l w , and can be minimized by using optimization algorithms such as gradient descent.

When more than two classes are involved, logistic regression is known as multinomial logistic regression. In multinomial logistic regression, instead of sigmoid, use the softmax function, which can be described mathematically as follows :. The softmax function produces the probabilities for each class so that the probabilities vector adds up to 1.

At the time of inference, the class with the highest softmax value becomes the output or predicted class. The loss function, as we discussed earlier, is the negative log-likelihood function, -l w , that can be minimized by the optimizers, such as gradient descent.

We will implement this loss function in the next section. In the following section, we will dig into our example for multiclass classification with logistic regression in TensorFlow. One of the most popular examples regarding multiclass classification is to label the images of handwritten digits. The images in the dataset appear as follows:.

There you go. We just trained our very first logistic regression model using TensorFlow for classifying handwritten digit images and got Keras is a high-level library that is available as part of TensorFlow. In this section, we will rebuild the same model we built earlier with TensorFlow core with Keras:. In the preceding code, we are loading the training images in memory before both the training and test images are scaled, which we do by dividing them by Using Keras, we can achieve higher accuracy.

This is because Keras internally sets many optimal values for us so that we can quickly start building models. To learn more about Keras and to look at more examples, refer to the book Mastering TensorFlow, from Packt Publications.

In this chapter, we briefly covered the TensorFlow library. We covered the TensorFlow data model elements, such as constants, variables, and placeholders, and how they can be used to build TensorFlow computation graphs. We learned how to create tensors from Python objects. Tensor objects can also be generated as specific values, sequences, or random valued distributions from various TensorFlow library functions.

We covered the TensorFlow programming model, which includes defining and executing computation graphs. These computation graphs have nodes and edges. The nodes represent operations and edges represent tensors that transfer data from one node to another.

We covered how to create and execute graphs, the order of execution, and how to execute graphs on multiple compute devices, such as CPU and GPU. We also learned about machine learning and implemented a classification algorithm to identify the handwritten digits dataset. The algorithm we implemented is known as multinomial logistic regression. We used both TensorFlow core and Keras to implement the logistic regression algorithm.

Starting from the next chapter, we will look at many projects that will be implemented using TensorFlow and Keras. TensorFlow 1. His work primarily involves the application of deep learning methods to a variety of Uber's problems, ranging from forecasting and food delivery to self-driving cars.

Previously, he has worked in a variety of data science roles at the Bank of America, Facebook, and other start-ups. He has a keen interest in teaching and has mentored over students in AI through various start-ups and bootcamps. Armando creates AI-empowered products by leveraging reinforcement learning, deep learning, and distributed computing.

Armando has provided thought leadership in diverse roles at small and large enterprises, including Accenture, Nike, Sonobi, and IBM, along with advising high-tech AI-based start-ups. Armando has authored several books, including Mastering TensorFlow, TensorFlow Machine Learning Projects, and Python Data Analysis, and has published research in international journals and presented his research at conferences. She completed her master's in electronics in and her PhD in She was awarded the Best Presentation Award at the Photonics international conference.

She has co-authored two books. She has more than 40 publications in international journals and conferences. Her present research areas include machine learning, artificial intelligence, deep reinforcement learning, and robotics.

About this book TensorFlow has transformed the way machine learning is perceived. Publication date: November Publisher Packt. Pages ISBN Download code from GitHub. Chapter 1. Overview of TensorFlow and Machine Learning.

What is TensorFlow? Previously, he has worked in a variety of data science roles at the Bank of America, Facebook, and other start-ups. He has a keen interest in teaching and has mentored over students in AI through various start-ups and bootcamps. Armando Fandango creates AI empowered products by leveraging deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership as Chief Data Scientist and Director at startups and large enterprises.

He has been advising high-tech AI-based startups. He has also published research in international journals and conferences. She has been teaching neural networks for twenty years. Her research areas include machine learning, AI, neural networks, robotics, and Buddhism and ethics in AI. She has co-authored the book, Tensorflow 1. Modern model servers allow you to deploy your models without writing web app code. Often, they provide multiple API interfaces like representational state transfer REST or remote procedure call RPC protocols and allow you to host multiple versions of the same model simultaneously.

We describe model deployment in Chapters 8 and 9. The last step of the machine learning pipeline is often forgotten, but it is crucial to the success of data science projects. We need to close the loop. We can then measure the effectiveness and performance of the newly deployed model. During this step, we can capture valuable information about the performance of the model. In some situations, we can also capture new training data to increase our datasets and update our model.

This may involve a human in the loop, or it may be automatic. We discuss feedback loops in Chapter Except for the two manual review steps the model analysis step and the feedback step , we can automate the entire pipeline.

Data scientists should be able to focus on the development of new models, not on updating and maintaining existing models. At the time of writing, data privacy considerations sit outside the standard machine learning pipeline.

We expect this to change in the future as consumer concerns grow over the use of their data and new laws are brought in to restrict the usage of personal data. This will lead to privacy-preserving methods being integrated into tools for building machine learning pipelines. We discuss several current options for increasing privacy in machine learning models in Chapter 14 :. Encrypted machine learning, where either the entire training process can run in the encrypted space or a model trained on raw data can be encrypted.

All the components of a machine learning pipeline described in the previous section need to be executed or, as we say, orchestrated , so that the components are being executed in the correct order.

Inputs to a component must be computed before a component is executed. The orchestration of these steps is performed by tools such as Apache Beam, Apache Airflow discussed in Chapter 11 , or Kubeflow Pipelines for Kubernetes infrastructure discussed in Chapter While data pipeline tools coordinate the machine learning pipeline steps, pipeline artifact stores like the TensorFlow ML MetadataStore capture the outputs of the individual processes.

In , a group of machine learning engineers at Google concluded that one of the reasons machine learning projects often fail is that most projects come with custom code to bridge the gap between machine learning pipeline steps.

These tools can be used to manage the machine learning pipeline tasks; they allow a standardized orchestration and an abstraction of the glue code between tasks.

While it might seem cumbersome at first to learn a new tool e. By not adopting standardized machine learning pipelines, data science teams will face unique project setups, arbitrary log file locations, unique debugging steps, etc.

The list of complications can be endless. Pipeline tools like Apache Beam, Apache Airflow, and Kubeflow Pipelines manage the flow of tasks through a graph representation of the task dependencies. As the example graph in Figure shows, the pipeline steps are directed. Directed graphs avoid situations where some tasks start without all dependencies fully computed. Since we know that we must preprocess our training data before training a model, the representation as a directed graph prevents the training task from being executed before the preprocessing step is completed.

Because of the two conditions being directed and acyclic , pipeline graphs are called directed acyclic graphs DAGs.

You will discover DAGs are a central concept behind most workflow tools. We will discuss more details about how these graphs are executed in Chapters 11 and To follow along with this book, we have created an example project using open source data. The data is taken from the Consumer Finance Protection Bureau.

The machine learning problem is, given data about the complaint, to predict whether the complaint was disputed by the consumer.