Artificial Neural Network2017-11-09T06:52:05+00:00

[vc_row][vc_column width=”2/3″][vc_custom_heading text=”Artificial Neural Network ” font_container=”tag:h2|font_size:32|text_align:left” 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   Artificial Neural Network

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Artificial Neural Network

Artificial Neural Network

Objective Of The Course Overall:-

The objective of this neural network training is to provide an introduction to artificial neural networks (ANN) theory and practical applications using TensorFlow.  The artificial-neural-network course focuses on building artificial neural network models in TesorFlow for typical problems. In this neural network training your will receive a full training for using TensorFlow, which is the most popular library for building your own neural network. This neural network training guarantees you that you will receive tools end theory to build your own neural network models from experts in the field.  

Neural network training is for anyone that wants to make a career in artificial neural networks.  It doesn’t matter if you are a computer scientist or just a creative coder without machine learning background.  In this neural network training, it is covered basic fundamentals of the state-of-the-art of artificial neural networks, and the basic of tensorflow and python. [/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%2Fartificial-intelligence-bootcamp-online%2F|||”][/vc_column_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – IN CLASS” 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%2Fartificial-intelligence-bootcamp-class%2F|||”][/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:$9999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 25th Oct 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: $4999
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Artificial Neural Network

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]

Artificial Neural Network

Artificial Neural Network

Artificial Neural Network

Artificial Neural Networks are algorithms to perform certain specific tasks like clustering, classification, pattern recognition, and so on.

Artificial Neural Networks are inspired by brain neurons.  Artificial neurons are configured to perform specific tasks.

Artificial Neural Networks resemble the human brain in the way the knowledge is acquired through learning and in the way knowledge is stored within inter-neuron connection strengths (which is known as synaptic weights).

Artificial Neural Networks can be viewed as weighted directed graphs in which artificial neurons are nodes, and directed edges with weights are connections between neuron outputs and neuron inputs.

The Artificial Neural Network receives information from the external world in the form of pattern and image in vector form. Each input is multiplied by its corresponding weights (which is a linear equation). Weights are the information used by the neural network to solve a problem (or to weight the relationship between the input variable with the output variable). Typically weight represents the strength of the interconnection between neurons inside the artificial neural network.

The weighted inputs are all summed up inside the artificial neuron. In case the weighted sum is zero, the bias variable is added to make the output not- zero( Bias has the weight and input always equal to “1”). It is used a threshold value to limit the sum from 0 to the threshold value. Also, it is used an activation function to get the desired output. There are linear as well as the nonlinear activation function. [/vc_column_text][vc_tta_accordion spacing=”2″ gap=”2″ c_icon=”chevron” active_section=”0″ no_fill=”true” collapsible_all=”true”][vc_tta_section title=”Different Types of Layers in Artificial Neural Network” tab_id=”1509098808756-85ada6d3-89ce”][vc_column_text]

  • Input layer – It contains Artificial Neurons (which receive input from the outside world on which network will learn)
  • Output layer – It contains units that represent the output information.
  • Hidden layer – These units are in between input and output layers. These layers transform the input into something that output unit could use in some way.

Most Artificial Neural Networks are fully connected that means to say each hidden neuron is fully linked to the every neuron in its previous layer (input) and to the next layer (output) layer.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Different Types of Artificial Neural Networks:” tab_id=”1509098673358-71f4b4e0-73b1″][vc_column_text]

  • Perceptron – is an artificial neural network with only one artificial neuron (hidden layers are not used in this artificial neural network), some input units and one output unit. It is also known as “single layer perceptron”.
  • Radial Basis Function Network – These networks are similar to forward Neural Network, but in this artificial neural network it is used a radial basis function as activation function of these neurons.
  • Multilayer Perceptron -In these networks, it is used more than one hidden layer of neurons, unlike single layer perceptron. These artificial neural networks are also known as Deep Feedforward Neural Networks.
  • Recurrent Neural Network – This type of Artificial Neural Network, the hidden layer neurons has self-connections. These Recurrent Neural Networks have memory of the older generated models. At any instance, hidden layer neuron receives activation from the lower layer as well as it previous activation value.
  • Long /Short Term Memory Network (LSTM) – This type of Artificial Neural Network in which memory cell is incorporated inside hidden layer neurons is called LSTM network. In this type of artificial neural network, the generated models are saved for a short period of time.
  • Hopfield Network – This type of artificial neural networks are fully interconnected in which each neuron is connected to every other neuron. The network is trained with input pattern by setting a value of neurons to the desired pattern. Then its weights are computed. The weights are not changed. Once trained for one or more patterns, the network will converge to the learned patterns. It is different from other Neural Networks.
  • Boltzmann Machine Network – These networks are similar to Hopfield network, but in this artificial neural network some neurons are input, while other are hidden in nature. The weights are initialized randomly and learn through back propagation algorithm.
  • Convolutional Neural Network – This artificial neural network is used for image recognition. This artificial neural network has images as input, hidden layers, fully connected layers, and output layers.
  • Modular Neural Network – In this artificial neural network, it is the combined structure of different types of the neural network like multilayer perceptron, Hopfield NetworkRecurrent Neural Network, and so on.
  • Physical Neural Network – In this type of Artificial Neural Network, electrically adjustable resistance material is used to emulate the function of synapse instead of software simulations performed in the neural network.

The neural network learns by adjusting its weights and bias (threshold) interactively to yield desired output. These are also called free parameters. For learning to take place, the Neural Network is trained first. The training is performed using a defined set of rules also known as the learning algorithm.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Types of Learning in Neural Network” tab_id=”1509097065603-92a40fd6-c101″][vc_column_text]Supervised Learning – In supervised learning, the training data is input to the network, and the desired output is known weights are adjusted until production yields desired value.

Unsupervised Learning – The input data is used to train the network whose output is known. The network classifies the input data and adjusts the weight by feature extraction in input data.

Reinforcement Learning – Here the value of the output is unknown, but the network provides the feedback whether the output is right or wrong. It is Semi-Supervised Learning.

Offline Learning – The adjustment of the weight vector and threshold is made only after all the training set is presented to the network. It is also called Batch Learning.

Online Learning – The adjustment of the weight and threshold is made after presenting each training sample to the network.

Artificial Neural Networks have four different uses: classification, prediction, clustering and association.

  • Classification – An Artificial Neural Network can be trained to classify given pattern or data set into predefined class. For this case, it is used Feedforward Networks.
  • Prediction – An Artificial Neural Network can be trained to produce outputs that are expected from given input. In financial sector, this type of artificial neural network is used for Stock market prediction.
  • Clustering – An Artificial Neural Network can be used to identify a unique feature of the data and classify them into different categories without any prior knowledge of the data. The artificial neural networks used for clustering are: Competitive networks, Adaptive Resonance Theory Networks and Kohonen Self-Organizing Maps.
  • Association – An artificial Neural Network can be trained to remember the particular pattern, so that when the noise pattern is presented to the network, the network associates it with the closest one in the memory or discard it. For example, an artificial neural network, called Hopfield Networks, performs recognition, classification, and clustering, and so on.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Artificial Neural Network for Pattern Recognition” tab_id=”1509097065907-29a65164-4d32″][vc_column_text]Artificial neural networks for Pattern recognition learn to distinguish patterns of interest from their background. These artificial neural networks learn patterns such as fingerprint image, a handwritten word, human face and speech signal.

There are two types of classification:

  • Supervised classification – The input pattern is identified as the member of a predefined class.
  • Unsupervised classification – The artificial neural network find the pattern given the input data.

The artificial Neural Network architectures used for Pattern Recognition are: Multilayer Perceptron, Kohonen SOM (Self Organizing Map) and Radial Basis Function Network (RBF).[/vc_column_text][/vc_tta_section][vc_tta_section title=”Advantages of Artificial Neural Network” tab_id=”1509097485925-2baac5ef-a9c9″][vc_column_text]The advantages of an artificial neural network are that an artificial neural network can perform tasks that a linear program cannot and find better solutions than other methods.  Another advantage is that an artificial neural network does not need to be reprogrammed. Artificial neural networks can be implemented in any application and performed without any problem.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Limitations of Artificial Neural Network” tab_id=”1509097552269-609af61f-dc0e”][vc_column_text]The limitations of an artificial neural network are that it needs the pre-training to find a model, it needs to be emulated and it requires high processing time for large artificial neural networks[/vc_column_text][/vc_tta_section][vc_tta_section title=”Artificial Neural Networks Approaches” tab_id=”1509097607324-db22a60f-0662″][vc_column_text]An approach of Artificial neural networks (ANN) consists in a network with initial weights of neurons in hidden layers with weights computed during unsupervised training of artificial networks neural networks made up of restricted Boltzmann machines (RBM) or autoencoders (AE). These stacked restricted Boltzmann machines (RBM) in the artificial neural network (ANN) and autoencoders (AE) are trained with a big amount of unlabeled data. The aim of such training is to find hidden patterns and relationships in the data. The initialization of neurons with weights (that were obtained during the pre-training) lead to find a solution close to the optimal. This pre-training decrease the number of labeled data and the epochs (iterations in the training or pre-training) during the training, which is an advantage in neural network training with a lot of data.

Another approach of artificial neural networks (ANN) for the initialization of hidden neurons consists of functions of neuron activation, methods of stabilization and neural network training. Artificial neural networks have a high efficiency in image recognition, analysis and classification of texts along with translation of spoken speech into different languages.

Actually, both neural-network training approaches mentioned above are actively used. Using these pre-training approaches, it is possible to have slightly better result with less computational resources and less data for training.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Are The Benefits of Artificial Neural Network Technology ?” tab_id=”1509097665063-78858a38-db6a”][vc_column_text]With artificial neural network technologies (such as chatbots and voice recognition), businesses dealing in customer service are given a much more efficient, cost-effective communications solution that minimizes call queues and ensures the customer’s problems are solved as quickly as possible.

Iphone Uses Neural Engine to Accelerate Artificial Neural Networks

The iPhone X set the path for technology for the next decade. The phone has a new innovation called “neural engine” which is part of the new A11 processor that Apple developed to power the iPhone X. The engine has circuits that accelerate artificial neural networks that are good at processing images and speech.

Apple said the neural engine would accelerate the algorithms that recognize your face to unlock the phone and transfer your facial expressions onto animated emoji. Chip experts say the neural engine could become central to the future of the iPhone.

An Artificial Neural Network Constructs 3D model of any 2D Face Picture

An artificial neural network can take 2D face picture and model it as 3D face. The artificial neural network was developed by a team of researchers from the U.K. at the University of Nottingham and Kingston University. The researchers were able to do it by training a Convolutional Neural Network (CNN) to generate 3D models from 2D images.

The researchers use 2D facial pictures as input of the artificial neural network to generate 3D facial models. The artificial neural network learned how to spot patterns between the 2D pictures and 3D models. Then, the artificial neural network is fed with new 2D face images, the artificial neural network is able to generate it, based on prior examples. Actually, the 3D faces look pretty similar.

Feeding the artificial neural network with 2D images and generating 3D model counterparts is a lot of work that went into this simultaneously entertaining and frightening breakthrough. Actually, it took about nine months to do this research.

Artificial Neural Networks for Writing

An artificial neural network generates sentences with the same level of “usefulness” as those written by real people.

Researchers have done some incredibly entertaining things with artificial neural networks to identify food in photos. Another use for this neural network training was to write surprisingly believable Yelp reviews using this technology.

Researchers from the University used existing restaurant reviews to teach an artificial neural network (Recurrent Neural Network) how to write its own reviews, and the results were that many people could not recognize those reviews as fake (or generated by artificial neural networks). Actually, the trained artificial neural networks generated sentences with the same level of user-perceived ‘usefulness’” as those written by real people.

The artificial neural network used a dataset of more than 4 million Yelp reviews that had been written by one million different reviewers, covering 144,000 restaurants in 11 cities and four countries.

After the neural network training, the study participants were asked to mark the reviews as being either real or fake, and to rate the usefulness of the review on a scale from 1 to 5. The results were that the artificial neural network generated reviews that were “effectively indistinguishable” from authentic ones, and their average usefulness score was 3.15 (which is close to the 3.28 usefulness average for real reviews).

An Artificial Neural Network Algorithm Beats Human Professionals Lip Reading

Researchers at Oxford University and Google (Joon Son Chung of Oxford University, Andrew Senior, Oriol Vinyals and Andrew Zisserman) developed an Artificial Neural Network (ANN) algorithm for lip reading, which is capable of deciphering more words than a professional human lip reader.

They created a lip reading artificial neural network (ANN) that actually outperformed a human professional. These researchers used Google’s DeepMind neural network and trained their artificial neural network (ANN) to lip read by using thousands of hours of BBC TV videos.

The disadvantage of this artificial neural network (ANN) for lip reading is that some experts fear that this technology could also lead to a new wave of spying attacks. For example, this artificial neural network technology could be used to “listen in” on a conversation from a distance.

Artificial Neural Network for Solving Rubik’s Cube

A convolutional neural network (CNN) (which are typical used in computer vision) is trained to solve a Rubik’s Cube. This CNN was created using TensorFlow (Google) and Keras (Python deep-learning library).

This CNN consists of two layers: a convolutional layer and a feedforward layer. MagicCube was used to simulate and visualize Rubik’s Cube, so the puzzle can be solved virtually.

The cube is randomly shuffled from a solved state to a random configuration (which is repeated ten times). These ten steps are the input of the CNN backwards. At each step, the artificial neural network guesses what the next move to solve the Rubik’s Cube, and then it is given to the artificial neural network the next move. These steps are repeated until the artificial neural network learns how to solve the Rubik’s Cube.

It was used Nvidia Geforce GTX 1050 graphics with a Pascal GPU for the neural network training (to accelerate the task). It was played 50,00 games (iterations of the Rubik’s Cube) with the CNN.

The CNN was tested with new random combinations and the artificial neural network solves the Rubik’s Cube in seconds. The Rubik’s Cube was solved six or seven moves away, for about 75% of the time. If the artificial neural network is fed with more moves than that (shuffling it more than seven times) the artificial neural network struggles to solve it. The artificial neural network struggles because the training data had only 10 steps of random combinations.

Actually, using reinforcement-learning approach is a useful approach for finding a way of exploiting the symmetries of the cube. Finding a way for training larger artificial neural networks would be also likely necessary.

Artificial Neural Network Applications

Artificial Intelligence and artificial neural networks will have a major impact in all industries, according to research conducted by BCG and   MIT Sloan Management Review. The research found that more than 70% of executives expect that Artificial Intelligence and artificial neural networks to play a significant role at their companies.

The development of autonomous vehicles and voice-activated home assistants are examples of artificial neural network technology. Health care is another example for this artificial neural network technology for disease diagnostic.  Over the next five years, it is expected that artificial neural network would gain significant traction in diagnosing illnesses. This technology already outperform leading radiologists at diagnosing some specific forms of cancer, and many startups and tech giants are working on AI-enabled methods to detect cancer even earlier and to provide ever more accurate prognoses. Artificial neural networks will accelerate the trend toward value-based health care. Finally, most companies are likely to buy at least some of their artificial neural network solution from technology vendors.

It is expected (in five years) more than half of customers will select services based on artificial neural network recommendations.

Artificial neural networks are taking on more sophisticated roles within technology interfaces. Actually, artificial neural networks are used in autonomous driving vehicles that use computer vision.  Artificial neural networks are used to translate into other languages. Artificial neural networks are making every interface both simple and smart.

Artificial neural networks will help accelerate technology adoption throughout their organizations. In addition, companies will invest extensively in artificial-neural-network-related technologies over the next three years.

Artificial Intelligence (included artificial neural networks) is making its first inroads into enterprise UI and UX with projects such as voice-activated systems. Advances in natural language processing and artificial neural network make technology more intuitive to use. Actually, artificial intelligence and artificial neural networks are used for telling virtual assistants to schedule a meeting instead of accessing scheduling software to find a time, create an event, and type the details,” they state. Artificial neural networks play a variety of roles throughout the user experience. Artificial neural networks can be used for applications like suggesting new music based on previous listening choices (mobile app Spotify). In other works, artificial neural networks are used to guide actions toward the best outcome.

The leading enterprise technology companies are using artificial neural network technology as the future of computer interfaces. These companies are Salesforce Einstein, Microsoft Azure Cognitive Services, Google Cloud Platform, and so on. There are many open source libraries for artificial neural network such as Google’s TensorFlow, Intel’s Trusted Analytics Platform, Caffe (which is a deep learning framework developed at the University of California, Berkeley).

Facebook  (using government census data and satellite images) and an artificial neural network for image recognition, the company can now locate every single man-made structure to within just five meters.  Facebook has determined that using drones and satellites will be most effective in pushing connectivity further. Actually, Facebook’s head of strategic innovation partnerships and sourcing.

Actually, Facebook and Microsoft team up to help artificial neural network technology more accessible to all developers. They created the Open Neural Network Exchange, which is known as ONNX.  These companies have worked together on the open source project to provide “a shared model representation for interoperability and innovation in the AI framework ecosystem.” Microsoft is planning to include ONNX in an upcoming release of the Cognitive Toolkit, its open source framework for building deep neural networks. Microsoft and Facebook plan to include more reference implementations, examples, and more in the near future.

For the foreseeable future, most companies will need to rely on internal data scientists to find, collect, collate, and create data sources and to develop and train company-specific Artificial Neural Network systems. Many Companies would outsource an entire AI process.

Actually, companies do not need to develop all of their AI machinery internally. Supporting platforms and services are available in the market, then these companies can rent raw computing power in the cloud or deploy it on the premises with specific hardware that can process many tasks in parallel (in the case of artificial neural networks). Companies can also access rapidly developing Artificial neural network architectures based on open-source code. Most cutting-edge Artificial neural network algorithms are available in the public domain, and leading scientists have pledged to continue to publish and open-source their work on these algorithms. In addition, businesses have made artificial neural network platforms, such as Google’s TensorFlow, available as a service.

Significant adoption of artificial neural networks in business remains low: only 1 in 20 companies has extensively incorporated Artificial neural networks or other AI technology, according to our survey with MIT. Nevertheless, every industry includes companies that are ahead of the pack.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Artificial Neural Networks Used to Guess Your Password” tab_id=”1509098211688-1d07d875-c1dc”][vc_column_text]Researchers used artificial neural networks to create a program that guesses more than a quarter of the passwords from a set of more than 43 million LinkedIn profiles.

This research could help users and companies to measure the strength of passwords. Actually, This new technique could also potentially be used to generate decoy passwords to help detect breaches.

Actually, this artificial neural network system is the strongest program for password guessing. Other methods for guessing passwords are: brute force approach (in which they randomly try a lot of combinations of characters until they get the right one).  Other approaches involve extrapolating from previously leaked passwords and probability methods to guess each character in a password based on what came before. The disadvantage is that these other approaches require many years of manual coding to build up their plans of attack.

This artificial neural network approach is a big advance for AI technology. Researchers at Stevens Institute of Technology in Hoboken, New Jersey, uses artificial neural networks to produce artificial outputs (like images) that resemble real examples (actual photos), while a “discriminator” tries to detect real from fake.

Stevens team compares the generator and discriminator to a police sketch artist and eye witness, respectively; the sketch artist is trying to produce something that can pass as an accurate portrait of the criminal. These artificial neural networks have been used to make realistic images, but have not been applied much to text.

The Stevens team created an artificial neural network called PassGAN and compared it with two versions of hashCat and one version of John the Ripper. The scientists fed each neural network with millions of leaked passwords from a gaming site called RockYou, and asked them to generate hundreds of millions of new passwords on their own. Then they counted how many of these new passwords matched a set of leaked passwords from LinkedIn, as a measure of how successful they’d be at cracking them.

The average salary for a neural network engineer is $149,465 per year.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Neural Network for Entrepreneurs” tab_id=”1509098316061-dbd6c948-6444″][vc_column_text]Artificial Intelligence (AI) is the biggest business opportunity and it is expected to generate &15.7 trillion by 2030. The growth of the global GDP is of 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. 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 of deep learning 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 artificial neural network has been created in the last two years.

Neural network training of Bigdataguys offers courses of artificial neural networks. You can acquire a job as neural network engineer in industries Google, Facebook, Uber, Microsoft, or any other company. The best way to learn Artificial Neural network is to take a course with us. Neural network training covers the basic theory and practical examples to create your own artificial neural network.

Many companies need people with neural network training to work with them in different types of problems. Bigdataguys gives you tools to artificial neural network.

Enroll to our neural network training.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][vc_separator color=”white” style=”dashed”][vc_custom_heading text=”CURRICULUM ” font_container=”tag:h4|font_size:24px|text_align:justify|color:%2303a9f4″ google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_tta_accordion color=”white” spacing=”2″ gap=”2″ active_section=”0″ no_fill=”true”][vc_tta_section title=”TENSORFLOW BASICS” tab_id=”1509099381494-94c9b140-24fc”][vc_column_text]Lecture1.1 Creation, Initializing, Saving, and Restoring TensorFlow variables
Lecture1.2 Feeding, Reading and Preloading TensorFlow Data
Lecture1.3 How to use TensorFlow infrastructure to train models at scale
Lecture1.4 Visualizing and Evaluating models with TensorBoard[/vc_column_text][/vc_tta_section][vc_tta_section title=”TENSORFLOW MECHANICS” tab_id=”1509099381805-60c7252a-359b”][vc_column_text]Lecture2.1 1.Inputs and Placeholders
Lecture2.2 2.Build the GraphS
Lecture2.3 Inference
Lecture2.4 Loss
Lecture2.5 Training
Lecture2.6 3.Train the Model
Lecture2.7 The Graph
Lecture2.8 The Session
Lecture2.9 Train Loop
Lecture2.10 4.Evaluate the Model
Lecture2.11 Build the Eval Graph
Lecture2.12 Eval Output[/vc_column_text][/vc_tta_section][vc_tta_section title=”THE PERCEPTRON” tab_id=”1509099527940-22396b80-5497″][vc_column_text]Lecture3.1 Activation functions
Lecture3.2 The perceptron learning algorithm
Lecture3.3 Binary classification with the perceptron
Lecture3.4 Document classification with the perceptron
Lecture3.5 Limitations of the perceptron
Lecture3.6 Minimizing the cost function
Lecture3.7 Forward propagation
Lecture3.8 Back propagation[/vc_column_text][/vc_tta_section][vc_tta_section title=”FROM THE PERCEPTRON TO SUPPORT VECTOR MACHINES” tab_id=”1509099680061-5428885e-af28″][vc_column_text]Lecture5.1 Nonlinear decision boundaries
Lecture5.2 Feedforward and feedback artificial neural networks
Lecture5.3 Multilayer perceptrons
Lecture5.4 Improving the way neural networks learn[/vc_column_text][/vc_tta_section][vc_tta_section title=”CONVOLUTIONAL NEURAL NETWORKS” tab_id=”1509099857097-9bf9c037-541d”][vc_column_text]Lecture6.1 Goals
Lecture6.2 Model Architecture
Lecture6.3 Principles
Lecture6.4 Code Organization
Lecture6.5 Launching and Training the Model
Lecture6.6 Evaluating a Model[/vc_column_text][/vc_tta_section][/vc_tta_accordion][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%2Fartificial-intelligence-bootcamp-online%2F|||”][/vc_column_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – IN CLASS” 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%2Fartificial-intelligence-bootcamp-class%2F|||”][/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: $4999
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” size=”lg” align=”right” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fartificial-intelligence-bootcamp-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: $9999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 25th Oct 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” size=”lg” align=”right” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fartificial-intelligence-bootcamp-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_custom_heading text=”COURSE HIGHLIGHTS” 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]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” shape=”round” color=”blue” 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″ shape=”round” color=”blue” 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” shape=”round” color=”blue” 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_text_separator title=””][/vc_column_inner][/vc_row_inner][vc_tta_pageable no_fill_content_area=”1″ autoplay=”10″ active_section=”1″ pagination_color=”white”][vc_tta_section title=”Section 1″ tab_id=”1500067469553-ae818e0b-9db2″][vc_single_image image=”19441″ img_size=”medium” alignment=”center”][vc_column_text]

Deep Learning with Tensor Flow In-Class or Online

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

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

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

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

[/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_tta_accordion][vc_tta_section title=”What is the duration of the course?” tab_id=”1507713849231-a93461c6-e3de”][vc_column_text]Advanced Course like Artificial Neural Network in TensorFlow Artificial Intelligence duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.

I am text block. Click edit button to change this text. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What If I Miss A Class?” tab_id=”1507713929018-7ee10f02-5a88″][vc_column_text]Neural Network Tutorial – record each LIVE class session you undergo through and we will share the recordings of each session/class.

I am text block. Click edit button to change this text. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.[/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=”1507714008942-13aa9e97-6a4a”][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=”1507714478844-d794cb19-9d26″][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=”1507714567438-c536d3be-dda1″][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=”1507714612333-57d649de-cf1b”][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=”1507714756060-1c275ceb-789e”][vc_column_text]The Artificial Neural Network in TensorFlow Artificial Intelligence 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-review-recap id=”0″][/vc_column_text][vc_text_separator title=”Comments” color=”custom” accent_color=”#029dff”][vc_wp_recentcomments number=”3″][/vc_column][/vc_row][vc_row][vc_column][vc_separator][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” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fblog%2F|||”][vc_masonry_grid post_type=”post” max_items=”3″ style=”lazy” items_per_page=”3″ show_filter=”yes” initial_loading_animation=”fadeIn” grid_id=”vc_gid:1510210064368-e5d77aab-fae7-1″ taxonomies=”5350″ filter_source=”category”][vc_separator][vc_custom_heading text=”INSTRUCTORS” font_container=”tag:h4|font_size:24px|text_align:center|color:%23029dff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%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.

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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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