ANN Powerpoint Presentation Slides

Slide 1: This slide introduces ANN. Commence by stating Your Company Name.
Slide 2: This slide depicts the Agenda of the presentation.
Slide 3: This slide elucidates the Table of contents.
Slide 4: This slide highlights the Title for the Topics to be covered in the next template.
Slide 5: This slide shows the Artificial Neural Network(ANN) learning institution, one of the best in the US; it works with students and advises on building technology based on the brain's complexity.
Slide 6: This slide illustrates the required education for learning ANN, which includes a bachelor's degree in electrical engineering and knowledge of the basics of statistics.
Slide 7: This slide mentions the Heading for the Components to be discussed further.
Slide 8: This slide provides an overview of ANN, the network similar to the human brain; dendrites represent inputs, nodes represent the cell nuclei, and so on.
Slide 9: This slide showcases the advantages of ANN, including the capability of parallel processing, data storage over the entire network, the ability to handle multiple data, etc.
Slide 10: This slide reveals the Title for the Ideas to be covered further.
Slide 11: This slide demonstrates the architecture of Artificial Neural Network, which includes three layers such as input, hidden, and output.
Slide 12: This slide depicts the three layers of ANN which form its architecture, including input, hidden, and output layer.
Slide 13: This slide displays the working of Artificial Neural Network, which includes three layers such as input, hidden, and output.
Slide 14: This slide continues the Working of artificial neural network.
Slide 15: This slide reveals the Heading for the Ideas to be discussed in the upcoming template.
Slide 16: This slide introduces an artificial neuron, the primary processing unit; it takes an input and sends output using the activation function.
Slide 17: This slide provides an overview of the activation function, which is very crucial for an ANN to understand the complexities of non-linear functional mappings within the input and output variables.
Slide 18: This slide depicts two types of activation function linear in which the function's output will not be restricted within range, and the other is non-linear, which allows the model to adapt to a wide range of data.
Slide 19: This slide shows three different non-linear activation functions based on curves and ranges, including sigmoid, tanh, and ReLU.
Slide 20: This slide talks about the sigmoid non-linear activation function, which is utilized in models which require the prediction of the possibility of an output.
Slide 21: This slide presents the tanh Activation Function ranging from -1 to 1, and both tanh and sigmoid are used in feedforward networks.
Slide 22: This slide demonstrates the rectified linear unit activation function, which is the most utilized function in the current world and is both derivative and monotonic.
Slide 23: This slide portrays the Title for the Components to be covered next.
Slide 24: This slide shows feedforward, one of the types of Artificial Neural Networks(ANN) in this; signals can go in only one way, and outputs of the previous layer become the input of the next one.
Slide 25: This slide depicts the feedback network, which is the other type of ANN; this contains feedback loops; in this, neurons, just like the brain, it can have any number of connections.
Slide 26: This slide mentions the Heading for the Topics to be covered next.
Slide 27: This slide introduces cost function in an artificial neural network(ANN); in general, it is the difference between the predicted and actual value.
Slide 28: This slide demonstrates the two processes to minimize the cost function, including backpropagation and forward propagation.
Slide 29: This slide highlights the Title for the Topics to be covered further.
Slide 30: This slide represents the applications of artificial neural networks such as paraphrase detection, forecasting, image processing, and so on.
Slide 31: This slide showcases the use cases of neural networks, including handwritten characters recognition, voice recognition, detection of fraud signatures, etc.
Slide 32: This slide indicates the Title for the Topics to be covered in the upcoming template.
Slide 33: This slide illustrates the two types of ANN techniques, including learning under supervision, and learning without supervision.
Slide 34: This slide outlines the Heading for the Components to be discussed further.
Slide 35: This slide shows the disadvantages of ANN, including the absence of assurance of proper structure of the network, no pre-determined duration of network development.
Slide 36: This slide indicates the Title for the Topics to be covered next.
Slide 37: This slide demonstrates the Training Schedule for ANN based on topics, mode of training and its training, along with assessment.
Slide 38: This slide depicts the fee for the certificate course of Artificial Neural Network on the basis of total course days, fees, and mode of payment.
Slide 39: This slide mentions the Heading for the Contents to be covered further.
Slide 40: This slide shows the 30-60-90 Days Plan for ANN course.
Slide 41: This slide elucidates the Title for the Components to be discussed next.
Slide 42: This slide portrays the roadmap for the Artificial Neural Network(ANN) learning course.
Slide 43: This is the Icons slide containing all the Icons used in the plan.
Slide 44: This slide is used to depict Additional information.
Slide 45: This slide represents the vision, mission & goal of the Company.
Slide 46: This slide incorporates the Bar chart.
Slide 47: This slide contains the Post it notes for reminders and deadlines.
Slide 48: This is the Thank You slide for acknowledgement.