#### TRAINING METHODOLOGY

**In Class:** $4,999

**Locations:** **NEW YORK CITY, D.C, BAY AREA.**

**Next Session:** 15th Jul 2017

**Online:** $2,499

**Next Session:** 15th Jul 2017

Home / All courses / Advance Deep learning / Deep Learning Tutorials | Tensorflow Tutorials

## Deep Learning Training |Tutorials | Tensorflow Tutorials

Instructor: John Doe, Lamar George

#### DESCRIPTION

**Deep Learning Training | Bootcamps/Workshops in NYC, Bay Area and on demand online **

**Deep learning Training | Prerequisites: **Statistics Python

This course deep learning training is for Python programmers and data analysts who want to learn more cutting edge machine learning techniques. Students should have basic experience with Python. Students with no prior Python experience can take our 1 week Python programming fast track course which covers sufficient Python for this course.

**Deep learning training |Artificial intelligence & Deep Learning with tensorflow (convolutional neural networks) **

**Description **

TensorFlow is a 2nd Generation API of Google’s open source software library for Deep Learning. The system is designed to facilitate research in machine learning and to make it quick and easy to transition from research prototype to production system.

**Audience for Deep Learning Training:**

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

**Deep learning Training Goals:**

After completing this course, delegates will understand TensorFlow’s structure and deployment mechanisms

Be able to carry out installation/production environment/architecture tasks and configuration

Be able to assess code quality, perform debugging, monitoring

Be able to implement advanced production like training models, building graphs and logging

AI & Deep learning with Tensorflow course will make you an expert in training and optimize basic and convolutional neural networks using real time projects and assignments. You will also master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM).

- TensorFlow could be a game-changer in the future of AI – Google Deep learning training
- Google gives every one machine learning superpowers with TensorFlow | deep learning training
- Google open-sources TensorFlow for deep learning with big data | Deep learning trainin g

**Hardware Requirements for deep learning training with tensorflow :**

The system requirements for Deep Learning with Tensorflow course is Multicore Processor (i3-i7 series), 8GB of RAM is recommended and 15 GB of free disk space. The operating system can be Windows, Linux or Mac OS X.

Deep learning training Hands-On Sessions (Lab Exercises) –

For executing the practical, you will set-up Tensorflow library on your machine, which can be installed on any operating system that is (Windows, Linux or Mac OS X). The detailed step by step installation guides will be present in your LMS which will help you to install and setup the required environment. In case you come across any doubt, the 24*7 support team will promptly assist you.

**Deep learning training | Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.**

We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

The average salary for deep learning with tensor-flow is $65,885*.

#### CURRICULUM

**Deep learning training | MACHINE LEARNING AND RECURSIVE NEURAL NETWORKS (RNN) BASICS **

Lecture1.1 NN and RNNLecture1.2 Deep Learning training

Lecture1.2 Backpropagation

Lecture1.3 Long short-term memory (LSTM) Deep Learning training

**TENSORFLOW BASICS Deep Learning Training**

Lecture2.1 Creation, Initializing, Saving, and Restoring TensorFlow variables Deep Learning training

Lecture2.2 Feeding, Reading and Preloading TensorFlow Data Deep Learning Training

Lecture2.3 How to use TensorFlow infrastructure to train models at scale

Lecture2.4 Visualizing and Evaluating models with TensorBoard Deep p Learning training

**Deep Learning training TENSORFLOW MECHANICS (ARTIFICIAL INTELLIGENCE)**

Lecture3.1 1. Prepare the Data Download Inputs and Placeholders

Lecture3.2 2. Build the Graph Inference Loss Training Deep Learning training

Lecture3.3 3 Train the Model The Graph The Session Train Loop

Lecture3.4 4 Evaluate the Model Build the Eval Graph Eval Output Deep Learning training

**ADVANCED USAGE Deep Learning training**

Lecture4.1 Threading and Queues

Lecture4.2 Distributed TensorFlow

Lecture4.3 Writing Documentation and Sharing your Model

Lecture4.4 Customizing Data Readers

Lecture4.5 Using GPUs¹ Deep Learning training

Lecture4.6 Manipulating TensorFlow Model Files

**TENSORFLOW SERVING (ARTIFICIAL INTELLIGENCE) | Deep Learning training**

Lecture5.1 Introduction

Lecture5.2 Basic Serving Tutorial

Lecture5.3 Advanced Serving Tutorial Deep Learning training

Lecture5.4 Serving Inception Model Tutorial

**Online:** $2499

**Next Batch: **starts from 17th July 2017

**In Class:** $4,999

**Locations:** New York City, D.C., Bay Area

**Next Batch: **starts from 17th July 2017

#### COURSE HIGHLIGHTS

**Skill level:** Intermediate

**Language:** English

**Certificate:** No

**Assessments:** Self

**Prerequisites:** Basic Python programming

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#### SOME COURSES YOU MAY LIKE

##### Deep Learning with Tensor Flow In-Class or Online

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

**Instructors:** John Doe, Lamar George** Duration:** 50 hours

**Lectures:**25

#### Neural Networks Fundamentals using Tensor Flow as Example Training (In-Class or Online)** **

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

**Instructors:** John Doe, Lamar George** Duration:** 50 hours

**Lectures:**25

#### Tensor Flow for Image Recognition Bootcamp (In-Class and Online)

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

**Instructors:** John Doe, Lamar George** Duration:** 50 hours

**Lectures:**25

#### OUR PRODUCTS

#### SOME OTHER COURSES YOU MAY LIKE

https://www.bigdataguys.com/courses/advanceddeeplearningtrainingcourse/Workday Financials

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Machine Learning With Tenser Flow

https://www.bigdataguys.com/courses/deep-learning-vision-caffe-bootcamp-online-class/

https://www.bigdataguys.com/courses/deep-learning-neural-networks-training/

#### FAQ'S

**What do I need to know before taking this Course?**

A basic understanding of Python and modeling.

Familiarity with matrices and linear algebra.

**Does Tensor Flow work with Python 3?**

As of the 0.6.0 release timeframe (Early December 2015), it does support Python 3.3+.

#### REVIEWS

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#### STUDENTS WHO VIEWED THIS COURSE ALSO VIEWED

Deep learning training | What’s the Difference Between Deep Learning Training and Inference?

School’s in session. That’s how to think about __deep neural networks__ going through the “training” phase. Neural networks get an education for the same reason most people do — to learn to do a job.

Deep Learning Trainin g | More specifically, the trained neural network is put to work out in the digital world using what it has learned — to recognize images, spoken words, a blood disease, or suggest the shoes someone is likely to buy next, you name it — in the streamlined form of an application. This speedier and more efficient version of a neural network *infers *things about new data it’s presented with based on its training. In the AI lexicon this is known as “inference.”

Inference is where capabilities learned during deep learning training are put to work.

Inference can’t happen without training. Makes sense. That’s how we gain and use our own knowledge for the most part. And just as we don’t haul around all our teachers, a few overloaded bookshelves and a red-brick schoolhouse to read a Shakespeare sonnet, inference doesn’t require all the infrastructure of its training regimen to do its job well.

So let’s break down the progression from training to inference, and in the context of AI how they both function.

**Deep learnin Training | Training Deep Neural Networks**

Just as we don’t haul around all our teachers, a few overloaded bookshelves and a red-brick schoolhouse to read a Shakespeare sonnet, inference doesn’t require all the infrastructure of its training regimen to do its job well.

Deep Learinin Training – While the goal is the same – knowledge — the educational process, or training, of a neural network is (thankfully) not quite like our own. Neural networks are loosely modeled on the biology of our brains — all those interconnections between the neurons. Unlike our brains, where any neuron can connect to any other neuron within a certain physical distance, __artificial neural networks have separate layers, connections, and directions of data propagation__.

When training a neural network, training data is put into the first layer of the network, and individual neurons assign a weighting to the input — how correct or incorrect it is — based on the task being performed.

In an image recognition network, the first layer might look for edges. The next might look for how these edges form shapes — rectangles or circles. The third might look for particular features — such as shiny eyes and button noses. Each layer passes the image to the next, until the final layer and the final output determined by the total of all those weightings is produced.

But here’s where the training differs from our own. Let’s say the task was to identify images of cats. The neural network gets all these training images, does its weightings and comes to a conclusion of *cat* or *not*. What it gets in response from the training algorithm is only “right” or “wrong.”

**Training Is Compute Intensive**

And if the algorithm informs the neural network that it was wrong, it doesn’t get informed what the right answer is. The error is propagated back through the network’s layers and it has to guess at something else. In each attempt it must consider other attributes — in our example attributes of “catness” — and weigh the attributes examined at each layer higher or lower. Then it guesses again. And again. And again. Until it has the correct weightings and gets the correct answer practically every time. It’s a cat.

Deep learning Training – Training can teach deep learning networks to correctly label images of cats in a limited set, before the network is put to work detecting cats in the broader world.

Now you have a data structure and all the weights in there have been balanced based on what it has learned as you sent the training data through. It’s a finely tuned thing of beauty. The problem is, it’s also a monster when it comes to consuming compute. Andrew Ng, who honed his AI chops at Google and Stanford and is now chief scientist at Baidu’s Silicon Valley Lab, says training one of Baidu’s Chinese speech recognition models requires not only four terabytes of training data, but also 20 exaflops of compute — that’s 20 billion *billion *math operations — across the entire training cycle. Try getting that to run on a smartphone – Deep learning Training

That’s where inference comes in.

**Deep Learning Training – Congratulations! Your Neural Network Is Trained and Ready for Inference**

That properly weighted neural network is essentially a clunky, massive database. What you had to put in place to get that sucker to learn — in our education analogy all those pencils, books, teacher’s dirty looks — is now way more than you need to get any specific task accomplished. Isn’t the point of graduating to be able to get rid of all that stuff?

If anyone is going to make use of all that training in the real world, and that’s the whole point, what you need is a speedy application that can retain the learning and apply it quickly to data it’s never seen. That’s inference: taking smaller batches of real-world data and quickly coming back with the same correct answer (really a prediction that something is correct).

While this is a brand new area of the field of computer science, there are two main approaches to taking that hulking neural network and modifying it for speed and improved latency in applications that run across other networks.

**How Inferencing Works | Deep Learning Training **

How is inferencing used? Just turn on your smartphone. Inferencing is used to put deep learning to work for everything from speech recognition to categorizing your snapshots.

The first approach looks at parts of the neural network that don’t get activated after it’s trained. These sections just aren’t needed and can be “pruned” away. The second approach looks for ways to fuse multiple layers of the neural network into a single computational step.

It’s akin to the compression that happens to a digital image. Designers might work on these huge, beautiful, million pixel-wide and tall images, but when they go to put it online, they’ll turn into a jpeg. It’ll be almost exactly the same, indistinguishable to the human eye, but at a smaller resolution. Similarly, with inference, you’ll get almost the same accuracy of the prediction, but simplified, compressed and optimized for runtime performance.

What that means is we all use inference all the time. Your smartphone’s voice-activated assistant uses inference, as does Google’s speech recognition, image search, and spam filtering applications. Baidu also uses inference for speech recognition, malware detection, and spam filtering. Facebook’s image recognition and Amazon’s and Netflix’s recommendation engines all rely on inference.

Deep Learning Training – GPUs, thanks to their parallel computing capabilities — or ability to do many things at once — are good at both training and inference.

Systems trained with GPUs allow computers to identify patterns and objects as well as — or in some cases, better than — humans (see “__Accelerating AI with GPUs: A New Computing Model__”).

After training is completed, the networks are deployed into the field for “inference” — classifying data to “infer” a result. Here too, GPUs — and their parallel computing capabilities — offer benefits, where they run billions of computations based on the trained network to identify known patterns or objects.

You can see how these models and applications will just get smarter, faster and more accurate. Training will get less cumbersome, and inference will bring new applications to every aspect of our lives. It seems the same admonition applies to __AI__ as it does to our youth — don’t be a fool, stay in school. Inference awaits.

Telephone No.202-897-1944