Convolutional Neural Networks2017-12-28T00:35:56+00:00

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[vc_row][vc_column][vc_column_text]Convolutional Neural Network [vc_row full_width=”stretch_row_content” content_placement=”middle” css=”.vc_custom_1500406358180{padding-top: 35px !important;padding-bottom: 35px !important;background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/06/Untitled-design-2-1.jpg?id=20151) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column][vc_column_text]

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[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column width=”2/3″][vc_custom_heading text=”Convolutional Neural Network” font_container=”tag:h2|font_size:32|text_align:justify” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:700%20bold%20regular%3A700%3Anormal”][vc_column_text]Home  AI – Tensor Flow Convolutional Neural Network [rwp-users-rating-stars id=”0″]

 

Convolutional Neural Network

Convolutional Neural Network

Objective of the course overall

The objective of this Convolutional Neural Network is to provide an introduction to Convolutional Neural Network theory and practical applications using tensorflow.  The Convolutional Neural Network focuses on building Convolutional Neural Network models in tensorflow for typical problems. In this Convolutional Neural Network, you will receive a full training for using tensorflow, which is the most popular library for building your own neural network. This Convolutional Neural Network guarantees you that you will receive all tools end theory needed to work as deep learning engineer from experts in the field.  [/vc_column_text][vc_row_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – ONLINE” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdeep-learning-training-course-online%2F||target:%20_blank|”][/vc_column_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – INCLASS” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdeep-learning-training-course-class%2F||target:%20_blank|”][/vc_column_inner][/vc_row_inner][/vc_column][vc_column width=”1/3″][vc_row_inner][vc_column_inner css=”.vc_custom_1499974829652{background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-e1499974695599.png?id=20193) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_custom_heading text=”TRAINING METHODOLOGY” font_container=”tag:h4|font_size:24|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dotted”][vc_column_text]In Class: $4,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 20th Nov 2017[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner css=”.vc_custom_1499974829652{background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-e1499974695599.png?id=20193) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column_text]Online: $2,499
Next Session: On Demand[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner css=”.vc_custom_1510150844909{background-color: #000000 !important;}”][vc_column_inner][vc_btn title=”Register” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”juicy-pink” shape=”round” align=”center” i_align=”right” i_icon_fontawesome=”fa fa-sign-in” button_block=”true” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fauth-register%2F|||”][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column width=”2/3″][vc_separator][vc_column_text]Home / All courses / Advance Deep learning /Convolutional Neural Networks

 

Convolutional Neural Networks

Instructor: John Doe, Lamar George [rwp-users-rating-stars id=”0″][/vc_column_text][vc_text_separator title=”DESCRIPTION” title_align=”separator_align_left” color=”custom” accent_color=”#0a57e8″][vc_column_text]

Convolutional neural networks

Convolutional neural networks

Convolutional Neural Network

Convolutional Neural Network  is for anyone that wants to make a career in Deep learning  (DL) and Artificial intelligence (AI).  It doesn’t matter if you are a computer scientist or just a creative coder without machine learning background.  In this Convolutional Neural Network , it is covered basic fundaments of the state-of-the-art of Convolutional Neural Network, and the basic of TensorFlow and python.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][vc_tta_accordion spacing=”2″ gap=”2″ c_icon=”chevron” active_section=”0″ no_fill=”true”][vc_tta_section title=”Saving Snow Leopards with Convolutional Neural Network” tab_id=”1509692079851-b681bded-93b3″][vc_column_text]It is used convolutional neural networks (artificial intelligence) to save the snow leopard, which is the large cat native to the mountain ranges of Central and South Asia, and this animal is a highly endangered species.  Actually, there are like 6500 snow leopards left in the wild. Deep learning engineers took a Convolutional Neural Network to make this CNN.

Indeed, the snow leopard is an elusive creature. Indeed, the snow leopards are difficult to study. Actually, scientists in order to gather data about the snow leopards, they used camera traps to capture more than 1 million images. Indeed, everyone that takes a Convolutional Neural Network can make this CNN.

Indeed, not all of those images are of snow leopards. Then, it is needed to classify those images as being of snow leopards, their prey, some other animal or nothing at all. A challenge in this project is that snow leopards have excellent camouflage, and can be difficult to spot even by experienced observers. Additionally, Microsoft’s Azure Machine Learning teamed up with the Snow Leopard Trust to build an automated classification system (to cut down on the 300 person-hours per camera survey, which have 100,000 images). Deep learning engineers took a Convolutional Neural Network to make this CNN.

Indeed, the (Artificial Intelligence) system is based on a convolutional neural network (CNN), which used is to classify the camera trap images. Actually, they are re-using a Convolutional Neural Network and transferring learning (that was applied to the ResNet-50 model), which has already been trained on images of objects, animals, and people. Then, the output from the upper layers of ResNet-50 is then used to build a traditional logistic regression model on human-classified images stored in Spark, using the open-source MMLSpark library. Deep learning engineers took a Convolutional Neural Network to learn this.

Then, it is transferred learning using this CNN (that was trained with a previous dataset). Indeed, this classifier is available to the researchers (using on-line service in Azure). The “Snow Leopard Classification as a Service” is available on-line. All the researchers (or people that took a Convolutional Neural Network ) can upload a file of images from the latest camera trap survey, and the CNN system automatically identifies those that include a snow leopard. Deep learning engineers took a Convolutional Neural Network to make this CNN.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network.

Additionally, it is important to take a Convolutional Neural Network to have better opportunities and get a deep learning job.

A Convolutional Neural Network is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Deep Learning (Convolutional Neural Network) – Global Market Outlook (2017-2023)” tab_id=”1509692079980-6f985f35-8a0e”][vc_column_text]Actually, the Global Deep Learning Market is accounted for $1.95 billion in 2016 and is expected to reach $72.10 billion by 2023 growing at a CAGR of 67.4% during the forecast period. Additionally, the Convolutional Neural Network  is a great opportunity for you.  

Indeed, The usage of deep learning technology among various industries such as automotive, advertisement, medical, and others is increasing. Furthermore, this factor will have a positive impact on the growth of this market includes. Additionally, increasing acceptance of cloud-based technology, high usage of deep learning in big data analytics, high R&D expansions for enhanced processing hardware for deep learning and rising applicability in healthcare and autonomous vehicles are fueling the market growth. Deep learning engineers took a Convolutional Neural Network  to make those applications.

Indeed, there is a big challenge in hardware and in deep learning technology, which is acting as a key barrier to the market. Actually, it is needed that the chipmakers design better chips to accelerate artificial neural networks ANNs (because they need to work in parallel). Additionally, the market has tremendous growth opportunity such as utilization of deep learning technology (convolutional neural network) in smartphones and medical image analysis. Indeed, there is a delay of neuromorphic technology for deep learning  (because deep learning is not enough accurate for this industry sector and the law doesn’t approve it because of that) and development of algorithms’ at a faster pace when compared to its hardware. Deep learning engineers took a Convolutional Neural Network  to know more about this field.

Actually, data mining segment is anticipated to grow at the highest CAGR during the forecast period attributed to growing utilization of deep learning in cybersecurity and database systems and data analytics. Indeed, North America commanded the largest market share due to the rising investments in neural networks and artificial intelligence.

The big companies that are using deep learning (including convolutional neural networks) are General Vision Inc., Sensory Inc., Baidu Inc., Nvidia Corporation, Intel Corporation, Google Inc., Skymind, Qualcomm Technologies, Inc, Hewlett Packard Enterprise, Microsoft Corporation, IBM Corporation, Advanced Micro, Devices, Inc., Clarifai, Inc., HyperVerge and Entilic. Deep learning engineers took a Convolutional Neural Network  work in those companies.

Indeed, for those big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Convolutional Neural Network (CNN) for Deep Learning” tab_id=”1509692374600-cf928dd4-4f25″][vc_column_text]Neural networks are basic building blocks for deep learning. There are many types of neural networks such as convolutional neural networks (CNN or Convnets), which are used for image recognition. Deep learning engineers took a Convolutional Neural Network  to learn about CNNs.

The convolutional neural networks (convnets) have been the major breakthroughs in the field of deep learning. Indeed, convolutional neural networks (convnets) perform really well for image recognition. Indeed, deep learning engineers (or developers that took our Convolutional Neural Network ) can also use convolutional neural networks for natural language processing and speech analysis.  Deep learning engineers took a Convolutional Neural Network  to build their own CNN.

For example, deep learning engineers (or developers that took our Convolutional Neural Network ) that are training a (convolutional neural network) classifier to identify a cat, they need to define input, hidden and output layers.

Indeed, a neural network takes features as inputs. For example, in our Convolutional Neural Network  it is taken image array as inputs. Then, the deep learning engineers (or developers that took our Convolutional Neural Network ) have a vector with a size of (image_width*image_height) as an input.

For example, the deep learning engineers (or developers that took our Convolutional Neural Network ) could take just 10*10 grid (here for understanding), but the image array size = Width*Height*Color.

Indeed, deep learning engineers (or developers that took our Convolutional Neural Network ) feed it to the model and train it (using backpropagation) using many images (training datasets). Indeed, the training phase has many iterations (epochs).

After the network is trained, the deep learning engineers (or developers that took our Convolutional Neural Network ) give another cat picture to predict (to get the probability of being a cat) to see if it gives the result as “cat” (high probability score).

Indeed, the neural network may not predict well and what if I gave black-and-white images as test images (assume the train set does not have black-and-white images). Deep learning engineers took a Convolutional Neural Network  to learn this.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.

Actually, the network might fail to give the highest probability score as this type of features (black-and-white) that deep learning engineers (or developers that took our Convolutional Neural Network ) did not train.

Deep learning engineers took a Convolutional Neural Network  to learn that the network understands the mapping between X (input) and y (output) but not the patterns in X (input).

Indeed, they deep learning engineers (or developers that took our Convolutional Neural Network ) found that the CNN model was able to predict well for cats on more than 3 test images.

Developers will learn on Convolutional Neural Network  that Convnets are used mainly to find patterns in an image, and it’s not needed to give (input) features. Indeed, the Convolutional Neural Network understands the relevant features by itself as it goes deep.

Deep learning engineers took a Convolutional Neural Network  to learn that the Convolutional Neural Networks are better than neural networks because neural networks don’t scale well for full-sized images.

In our Convolutional Neural Network , you will learn how to initialize a convnet.  Indeed, it is created many images (from 1 image) by applying some filters, which are called weights, kernels or features. Indeed, convnets are initialized (with random weight values), and then during the training phase, these weights will get updated (the network learns these weights). Indeed, the filters are applied to image chunks. Actually, it is taken a local receptive field in the image (one image chunk) and it is applied the dot product to a scalar value, then it is moved the window by the stride and repeated the same process for the entire image. The next step of applying the filter (or kernel) to the image chunks is pooling, which is used to reduce the size of an image by taking the max values in the window (the multiplication of the kernel and image chunk). Indeed, padding is not necessary here, but for Padding explanation purpose only I added here. Deep learning engineers took a Convolutional Neural Network  to make this CNN.

Additionally, the next step is the normalization step, which is the step when it is applied an activation function. Indeed, the most used activation function is ReLu (Rectified linear unit). Indeed, it is used to don’t have negative values. Indeed, if the input is less than 0, a rectified linear unit has output 0 (and raw output otherwise). That is, the output is equal to the input if the input is greater than 0. Deep learning engineers took a Convolutional Neural Network  to make this CNN.

Indeed, the next step is to feed these values to a Fully Connected Neural network (which you can learn about it in our Convolutional Neural Network ).

Indeed, it is trained the model for all the images in the training set for certain no of epochs, and during training, we update the weights using backpropagation.

Deep learning engineers took a Convolutional Neural Network  to learn that a Convolution neural network is a network of different types of layers sequentially connected together. Indeed, these types of layers are:

  • Convolution layer (where the convolution process happens).
  • Pooling layer (where the pooling process happens).
  • Normalization layer (where the activation Rectified linear unit (ReLu) process happens).
  • Fully Connected layer (called Dense layer)

Deep learning engineers took a Convolutional Neural Network  to learn that a convolutional neural network CNN can have multiple convolution, pooling, normalization, and dense layers and not necessarily following the order.

In order to build your own CNN, it is very important to take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Baidu puts open source deep learning into smartphones (Computer vision, deep learning, and the camera in your phone)” tab_id=”1509692572910-dbdac634-f488″][vc_column_text]Actually, Baidu open sourced its PaddlePaddle deep learning suite. Indeed, Baidu has dropped another piece of AI tech into the public domain, which is a project to put AI on smartphones.

Indeed, Mobile Deep Learning (or MDL) is available on GitHub under the MIT license a day ago, along with the exhortation “Be all eagerness to see it”.

Indeed, Mobile Deep Learning (MDL) is a convolution-based neural network designed to fit on a mobile device. Indeed, MDL is used to build applications such as recognizing objects in an image using a smartphone’s camera. Deep learning engineers took a Convolutional Neural Network  to make this CNN.

Indeed, the neural network’s calculations are offloaded to a phone’s GPU. Additionally, Baidu said it has high speed and low complexity. Indeed, MDL runs on either iOS or Android, the MLD’s documentation leans more towards Apple than Google, with GPU support still an Android to-do, along with TensorFlow model support.

Indeed, MLD’s code fits in around 4 MB with no third-party library dependencies. The developers recommend the Baidu’s PaddlePaddle model for use with MDL, but it can also use Caffe (which is a deep learning framework).

Furthermore, another MDL example is an application for identifying the pattern on a china teacup, and it is used to find matching products for sale. Indeed, MDL supports MobileNet, SqueezeNet, and GoogLeNet.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Deep Learning (Convolutional Neural Network) – IMPACTS” tab_id=”1509692655771-11f48fd6-8e4b”][vc_column_text]Deep learning (which is what you learn with our Convolutional Neural Network ) is already having a big impact in the data center. Indeed, chipmakers add neural engines to mobile processors (to accelerate neural networks such as the convnet of our Convolutional Neural Network ). Indeed, companies such as Qualcomm, Intel and others are taking very different approaches.

Indeed, the new iPhone X (Apple) have the A11 processor’s new neural engine, which powers facial recognition and other features. Indeed, Huawei announced the Kirin 970 (which is the latest flagship processor). Indeed, the Kirin 970 is equipped with a Neural Processing Unit capable of processing images 20 times faster than the CPU alone. Deep learning engineers took a Convolutional Neural Network  to know more about CNNs.

Indeed, the sudden interest in neural engines is driven by the rise of deep learning. Additionally, these specialized processors are designed specifically to run complex algorithms used in artificial neural networks (ANNs) faster and more efficiently than general-purpose CPUs. Deep learning engineers took a Convolutional Neural Network  to learn this.

Indeed, deep learning is already having a profound impact in the data center. Actually, Nvidia’s graphics processors for games have found a second life as accelerators for training these complex models. Indeed, Microsoft is now using hundreds of thousands of FPGAs (field-programmable gate arrays) to power many of its services. Indeed, Microsoft will make that available to other customers through Azure. Actually, Google took things a step further and built its own Tensor Processing Units. Indeed, there are plenty of start-ups pursuing the same idea. Deep learning engineers took a Convolutional Neural Network  to work in those start-ups.

Indeed, now neural engines are migrating to the cloud. Actually, most of the heavy lifting is done in the cloud, but there are some applications that require lower latency. Furthermore, the most relevant examples are applications such as autonomous vehicles or smart surveillance systems where decisions need to make in near real-time. Indeed, are many AI applications that can benefit from local processing (like Apple’s Face ID).

Indeed, mobile deep learning applications are challenging because of the power limitations. Actually, chipmakers are adopting different strategies to address this problem. Deep learning engineers took a Convolutional Neural Network  to learn this.

Indeed, Qualcomm wants to take full advantage of all the resources that are already crammed onto its Snapdragon mobile SoCs. Indeed, Qualcomm has been experimenting with novel hardware for about a decade, and they discovered that rapid advances in the CPU, GPU, and Hexagon DSPs have largely eliminated the short-term need for specialized hardware for tasks such as computer vision and natural-language processing.

Qualcomm launched Snapdragon Neural Processing Engine (NPE), which is a set of software tools that takes models trained in Caffe, Caffe2 or TensorFlow. Indeed, NPE converts them into Qualcomm’s format to run across the Kryo CPU (32-bit FP), Adreno GPU (16-bit) or Hexagon DSP (8-bit integer) in Snapdragon 800 and 600 series processors. Furthermore, Qualcomm also has math libraries for neural networks including QSML (Qualcomm Snapdragon Math Library) and nnlib for Hexagon DSP developers.

Indeed, Qualcomm needs to specialized hardware, and it needs something like the HvX modules added to the Hexagon DSP to accelerate 8-bit fixed operations for inference. Indeed, Brotman said mobile SoCs will need specialized processors with tightly-coupled memory and an efficient dataflow (fabric interconnects) for neural networks.

Indeed, Qualcomm‘s competitors are designing chips for neural processing. Indeed, Apple and Huawei are designing chips for ANNs (artificial neural networks). Actually, the Samsung Exynos 8895 that powers the Galaxy S8 and Note 8 in many parts of the world has a Vision Processing Unit that speeds up motion detection and object recognition. Indeed, Ceva, Cadence Tensilica, Synopsys and others offer processor IP designed to speed up convolution neural networks for image recognition at the edge.

Indeed, Intel provides a complete AI ecosystem that spans from the data center to the cloud. Indeed, Intel notes correctly that Xeon servers are involved in nearly all training and inferencing workloads (using Nvidia Tesla GPUs to speed up the Artificial Neural Networks). Indeed, a derivative of the current Knights Landing processor that will deliver four times the performance of the Xeon Phi 7290 due to two new features, support for quad FMA (fused-multiply add) instructions and variable precision.

Furthermore, Lake Crest, which is a co-processor based on an entirely different architecture from last year’s acquisition of Nervana, is a distributed, dense linear algebra processor designed specifically to run dataflow graphs. Indeed, Intel hasn’t shared many details yet. Indeed, Intel is designing a tile of small tensor cores with lots of on-die memory (SRAM) in a multi-chip package with four 8GB stacks of second-generation High-Bandwidth Memory. Furthermore, the Nervana engine will deliver a boost (in training performance).

Indeed, Intel acquires Altera for $16.7 billion. Actually, Intel has a leading position in FPGAs for inferencing. Furthermore, Microsoft developed the Project Brainwave “soft DPU” (DNN processing unit) with a 14nm Stratix 10 FPGA  that will deliver “real-time AI” with a peak performance of 90 teraops using Microsoft’s limited-precision format.

Indeed, Intel is packaging Skylake Xeon Scalable processors with FPGAs using its proprietary EMIB (Embedded Multi-Die Interconnect Bridge) 2.5D technology. Actually, Intel has made two other acquisitions to expand its AI edge portfolio including Israel’s Mobileye (which is the leader in ADAS and autonomous driving) and Movidius (which has a vision processor that delivers more than one teraflop using less than one watt of power).

Actually, there are many deep learning frameworks such as Caffe, TensorFlow, Torch and PyTorch, Microsoft’s Cognitive Toolkit for training and running models. Actually, the hardware underneath is heterogeneous and is becoming more specialized.

Indeed, Google or Microsoft can design their own hardware and software to deliver particular services. Actually, it is a big challenge for developers who want to create accelerated applications that can run neural networks across all mobile devices. Additionally, Facebook seems particularly worried about this challenge. Actually, Facebook has previously open-sourced the Caffe2Go framework for mobile devices and at its recent @Scale conference. Indeed, Facebook was demonstrating how OpenGL could be used to speed up image recognition and special effects on a phone, but the API is old and hard to program.

Actually, there is clearly a need for some mobile inferencing standards and ARM that has taken some steps in this direction with its Compute Library with OpenCL and Neon. Indeed, it is needed to design chips that deliver the best solutions for deep learning.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Your First Machine Learning Model” tab_id=”1509692883722-4ccb5734-f37a”][vc_column_text]Indeed, Machine learning is the new hype and many companies are scrambling to hire data scientists to start making sense of their data. Indeed, this data can be images, emails, financial data and so on. Indeed, it is easy to get started with data science and machine learning.

Indeed, images are one of the easier to deal with. It is used a convolution neural network (CNN) for image recognition. Deep learning engineers (or people that took our convolutional neural network) can easily be trained to identify what is the content of the image. Indeed, convolutional neural network (CNNs) are a type of Neural Network, which is consists of convolution layers, pooling layers, normalization layers, and dense layers.

Indeed, the difference between a convolutional neural network (CNN) and a normal neural network is that the CNN has multiple “hidden” layers (and that’s the reason it is called a deep neural network). Indeed, it uses the mathematical function of Convolution to identify features in an image. Additionally, deep engineers (or people that took our convolutional neural network) can easily build their own neural network to apply optical character recognition (OCR) and identify text.

Data engineers (or people that took our convolutional neural network) use the following tools or languages to build a convolutional neural network or a simple artificial neural network:

  • Python programming language
  • Data Science Experience
  • TensorFlow

Data engineers (or people that took our convolutional neural network) use Python, which is a programming language, and the best language for data science in general.

Indeed, Data engineers (or people that took our convolutional neural network) use Data Science Experience (DSX), which is a notebook on the cloud that allows you to write code and description of the code, which is shareable and reproducible. Also, this DSX runs on the cloud, which provides you with the processing power required to run and train complex models.

Data engineers (or people that took our convolutional neural network) use TensorFlow, which is an open source library available in python and C. Indeed, TensorFlow runs in DSX, which provides you with the tools needed to quickly and easily write and train machine-learning models.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”ST preps second neural network IC” tab_id=”1509692993504-74379b18-ce8b”][vc_column_text]Actually, STMicroelectronics is designing a second iteration of the neural networking technology.

Actually, the technology could be used to distribute artificial intelligence throughout a system based on ST32 microcontrollers and sensors.

Actually, ST is now working on a new implementation of the technology for accelerating convolutional neural networks (you can learn about this on our convolutional neural network ). This new implementation is more optimized and more targeted.

Indeed, for big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Convolutional Neural Network for Entrepreneurs” tab_id=”1509693057309-d23ad87c-3f86″][vc_column_text]Artificial Intelligence (AI) (including Deep Learning) is the biggest business opportunity and it is expected to generate &15.7 trillion by 2030. The growth of the global GDP is 14%.

The expected productivity gain would be $6.6 trillion. The industries that would produce this productivity gain are robotics, autonomous vehicles or automated intelligent services (which include Convolutional Neural Network). Other companies that are part of this productivity gain are the ones that do business scenario simulations or decision-making support. It is expected that the demand for deep learning (which includes Convolutional Neural Network) would increase from higher quality products, which will be more intelligent and better adapted to the specific customer needs.  Actually, 90% of the market of deep learning has been created in the last two years. This means that the companies need people trained in deep learning and that take courses (such as our Convolutional Neural Network ).

The Convolutional Neural Network  of Bigdataguys is a great course to get a job as deep learning engineer in companies such as Google, Facebook, Uber, or any other company. The best way to learn about Convolutional Neural Networks is to take a course with us. The Convolutional Neural Network  covers the basic theory and practical examples to create your own Convolutional Neural Network.

Indeed, for those big companies, it is very important that their data engineers take a Convolutional Neural Network .

Additionally, it is important to take a Convolutional Neural Network  to have better opportunities and get a deep learning job.

A Convolutional Neural Network  is a great start to build your own CNN.

The average salary for “deep learning” ranges from approximately $109,038 per year for Scientist to $149,602 per year for Machine Learning Engineer.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][vc_text_separator title=”” title_align=”separator_align_left” color=”custom” accent_color=”#0a57e8″][vc_custom_heading text=”CURRICULUM ” font_container=”tag:h4|text_align:center”][vc_tta_accordion color=”white” spacing=”2″ gap=”2″ active_section=”0″ no_fill=”true”][vc_tta_section title=”MACHINE LEARNING AND RECURSIVE NEURAL NETWORKS (RNN) BASICS” tab_id=”1509693170677-bc28a435-1520″][vc_column_text]Lecture1.1 NN and RNNLecture1.2

Lecture1.2 Backpropagation

Lecture1.3 Long short-term memory (LSTM)[/vc_column_text][/vc_tta_section][vc_tta_section title=”TENSORFLOW BASICS” tab_id=”1509693170887-e39541e8-810b”][vc_column_text]Lecture2.1 Creation, Initializing, Saving, and Restoring TensorFlow variables

Lecture2.2 Feeding, Reading and Preloading TensorFlow Data

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

Lecture2.4 Visualizing and Evaluating models with TensorBoard[/vc_column_text][/vc_tta_section][vc_tta_section title=”TENSORFLOW MECHANICS (ARTIFICIAL INTELLIGENCE)” tab_id=”1509693274284-d85de97b-17f7″][vc_column_text]Lecture3.1 1. Prepare the Data Download Inputs and Placeholders

Lecture3.2 2. Build the Graph Inference Loss 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[/vc_column_text][/vc_tta_section][vc_tta_section title=”ADVANCED USAGE” tab_id=”1509693322832-eb44a135-52cc”][vc_column_text]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

Lecture4.6 Manipulating TensorFlow Model Files[/vc_column_text][/vc_tta_section][vc_tta_section title=”TENSORFLOW SERVING (ARTIFICIAL INTELLIGENCE)” tab_id=”1509693357003-04b51ac1-8dd8″][vc_column_text]Lecture5.1 Introduction

Lecture5.2 Basic Serving Tutorial

Lecture5.3 Advanced Serving Tutorial

Lecture5.4 Serving Inception Model Tutorial[/vc_column_text][/vc_tta_section][/vc_tta_accordion][vc_text_separator title=”” title_align=”separator_align_left” color=”custom” accent_color=”#0a57e8″][vc_column_text][/vc_column_text][vc_row_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – ONLINE” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdeep-learning-training-course-online%2F||target:%20_blank|”][/vc_column_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – INCLASS” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdeep-learning-training-course-class%2F||target:%20_blank|”][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner width=”1/2″][/vc_column_inner][vc_column_inner width=”1/2″][/vc_column_inner][/vc_row_inner][vc_row_inner css=”.vc_custom_1500072288565{margin-right: 10px !important;background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-e1499974695599.png?id=20193) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column_inner width=”2/3″][vc_column_text]Online: $2,499
Next Batch:On Demand[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/3″][vc_btn title=”ENROL NOW” style=”outline-custom” outline_custom_color=”#ffffff” outline_custom_hover_background=”#029dff” outline_custom_hover_text=”#ffffff” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdeep-learning-training-course-online%2F||target:%20_blank|”][/vc_column_inner][/vc_row_inner][vc_row_inner css=”.vc_custom_1500072200986{margin-right: 10px !important;background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/bh.png?id=20198) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column_inner width=”2/3″][vc_column_text]In Class: $4,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 20th Nov  2017
[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/3″][vc_btn title=”ENROL NOW” style=”outline-custom” outline_custom_color=”#ffffff” outline_custom_hover_background=”#029dff” outline_custom_hover_text=”#ffffff” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdeep-learning-training-course-class%2F||target:%20_blank|”][/vc_column_inner][/vc_row_inner][/vc_column][vc_column width=”1/3″ css=”.vc_custom_1499982058886{background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-1.png?id=20194) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_separator color=”white” style=”dotted”][vc_column_text]Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming[/vc_column_text][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”SCHEDULE YOUR FREE DEMO” font_container=”tag:h4|font_size:24|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal” link=”url:https%3A%2F%2Fcalendly.com%2Fbigdataguysadmin%2F15min||target:%20_blank|”][vc_btn title=”click to schedule” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” align=”center” i_icon_fontawesome=”fa fa-calendar” button_block=”true” add_icon=”true” link=”url:https%3A%2F%2Fcalendly.com%2Fbigdataguysadmin%2F15min||target:%20_blank|”][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”TALK TO US ” font_container=”tag:h4|font_size:24|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_btn title=”+1 202-897-1944″ style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” align=”center” i_icon_fontawesome=”fa fa-volume-control-phone” button_block=”true” add_icon=”true”][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”NEED CUSTOM TRAINING FOR YOUR CORPORATE TEAM?” font_container=”tag:h4|font_size:20|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fcontactus%2F||target:%20_blank|”][vc_btn title=”GET QUOTE” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” align=”center” button_block=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fcontactus%2F||target:%20_blank|”][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”NEED HELP? MESSAGE US” font_container=”tag:h4|font_size:24|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dotted”][vc_column_text]

[/vc_column_text][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner][vc_custom_heading text=”SOME COURSES YOU MAY LIKE” font_container=”tag:h4|font_size:24px|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dashed”][/vc_column_inner][/vc_row_inner][vc_tta_pageable no_fill_content_area=”1″ autoplay=”3″ active_section=”1″ pagination_color=”white”][vc_tta_section title=”Section 1″ tab_id=”1500067469553-ae818e0b-9db2″][vc_single_image image=”19441″ img_size=”full” alignment=”center”][vc_column_text]

Deep Learning in convolutional neural networks 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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Section 2″ tab_id=”1500067469854-6c4f938f-a6a8″][vc_single_image image=”19327″ img_size=”full” alignment=”center”][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Section” tab_id=”1500067796681-c32c5033-4285″][vc_single_image image=”19051″ img_size=”full” alignment=”center”][vc_column_text]

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

[/vc_column_text][/vc_tta_section][/vc_tta_pageable][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”OUR PRODUCTS” font_container=”tag:h4|font_size:24px|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dashed”][vc_column_text]VIDEOS
CONSULTING
PRODUCTS
IOT PRACTICE
IOT PRODUCTS
SECURITY[/vc_column_text][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”SOME OTHER COURSES YOU MAY LIKE” font_container=”tag:h4|font_size:24px|text_align:left|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dashed”][vc_column_text]Machine Learning AI
Machine Learning With Tenser Flow
Google Cloud Platform Big Data & Machine Learning

Deep Learning with TensorFlow

The Complete Bitcoin Course: Get .001 Bitcoin In Your Wallet
Intro to Dash Crypto Currency[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column width=”1/2″][vc_text_separator title=”FAQ’S” color=”custom” accent_color=”#029dff”][vc_column_text][/vc_column_text][vc_tta_accordion active_section=”1″][vc_tta_section title=”What is the duration of the course?” tab_id=”1509694069923-06a9a101-5956″][vc_column_text]Advanced Course like Convolutional Neural Network duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What If I Miss A Class?” tab_id=”1509694070619-42cdb8bc-6c64″][vc_column_text]We record each LIVE class session you undergo through and we will share the recordings of each session/class.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Can I Request For A Support Session If I Find Difficulty In Grasping Topics?” tab_id=”1509694071316-edc09354-62e8″][vc_column_text]If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Kind Of Projects Will I Be Working On As Part Of The Training?” tab_id=”1509694072148-e92f682e-5f79″][vc_column_text]You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.[/vc_column_text][/vc_tta_section][vc_tta_section title=”How Will I Execute The Practical?” tab_id=”1509694072847-3d311107-6a96″][vc_column_text]Our Trainers will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Are These Classes Conducted Via Live Online Streaming?” tab_id=”1509694452939-45223703-104f”][vc_column_text]Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Will the course fetch me a job?” tab_id=”1509694486529-6fbbc698-c2d6″][vc_column_text]The Convolutional Neural Network  by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][vc_column width=”1/2″][vc_text_separator title=”REVIEWS” color=”custom” style=”dashed” accent_color=”#0a57e8″][vc_column_text][rwp_box_recap id=”0″][/vc_column_text][vc_row_inner][vc_column_inner][vc_text_separator title=”COMMENTS” color=”custom” style=”dashed” accent_color=”#0a57e8″][vc_wp_recentcomments number=”3″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator color=”custom” accent_color=”#029dff”][vc_custom_heading text=”BLOGS” font_container=”tag:h4|font_size:24|text_align:center|color:%23000000″ google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:700%20bold%20regular%3A700%3Anormal”][vc_masonry_grid post_type=”post” max_items=”3″ grid_id=”vc_gid:1510150790384-77a3ef57-8b81-5″ taxonomies=”2″][vc_separator color=”custom” accent_color=”#029dff”][vc_custom_heading text=”INSTRUCTORS ” font_container=”tag:h4|font_size:24|text_align:center|color:%23000000″ google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:700%20bold%20regular%3A700%3Anormal”][/vc_column][/vc_row][vc_row][vc_column width=”1/2″][vc_column_text]

John Doe
Learning Scientist & Master Trainer John Doe has been a professional educator for the past 20 years. He’s taught, tutored, and coached over 1000 students, and he holds degrees in Physics and Literature from Northwestern University. He has spent the last 4 years studying how people learn to code and develop applications.

[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_column_text]

Lamar George
Learning Scientist & Master Trainer He has been a professional educator for the past 20 years. He’s taught, tutored, and coached over 1000 students, and he holds degrees in Physics and Literature from Northwestern University. He has spent the last 4 years studying how people learn to code and develop applications.

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