How to Build a Neural Network and Make PredictionsDeveloper Helps
Lately, people have been really into neural networks. They’re like a computer system that works like a brain, with nodes connected together. These networks are great at sorting through big piles of data and figuring out patterns to solve hard problems or guess stuff. And you know what’s super cool? They can keep on learning forever.
Creating and deploying neural networks can be a challenging process, which largely depends on the specific task and dataset you’re dealing with. To succeed in this endeavor, it’s crucial to possess a solid grasp of machine learning concepts, along with strong programming skills. Additionally, a deep understanding of the chosen deep learning framework is essential. Moreover, it’s imperative to prioritize responsible and ethical usage of AI models, especially when integrating them into real-world applications.
Learn from : https://www.developerhelps.com/how-to-build-a-neural-network-and-make-predictions/
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
How to Build a Neural Network and Make PredictionsDeveloper Helps
Lately, people have been really into neural networks. They’re like a computer system that works like a brain, with nodes connected together. These networks are great at sorting through big piles of data and figuring out patterns to solve hard problems or guess stuff. And you know what’s super cool? They can keep on learning forever.
Creating and deploying neural networks can be a challenging process, which largely depends on the specific task and dataset you’re dealing with. To succeed in this endeavor, it’s crucial to possess a solid grasp of machine learning concepts, along with strong programming skills. Additionally, a deep understanding of the chosen deep learning framework is essential. Moreover, it’s imperative to prioritize responsible and ethical usage of AI models, especially when integrating them into real-world applications.
Learn from : https://www.developerhelps.com/how-to-build-a-neural-network-and-make-predictions/
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
Aaa ped-23-Artificial Neural Network: Keras and TensorfowAminaRepo
We will focus in this part on two important libraries for ANN: Tensorflow and Keras. Both of them propose two types of model creation. We will use the high level API of tenshorflow, and the sequential models of Keras.
We will introduce you to some basic important concept related to tensorflow, and we will present you tensorboard. The later one is used to visualize, among other things, quantitative values related to a training process.
[Notebook](https://colab.research.google.com/drive/13KlhoNvYmeRZTZ-TLKAtW3rOkFzQVGYC)
The Impact of Cloud Computing on Predictive Analytics 7-29-09 v5Robert Grossman
This is a talk I gave in San Diego on July 29, 2009 explaining some of the impact and some of the opportunities of cloud computing on predictive analytics.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
Aaa ped-23-Artificial Neural Network: Keras and TensorfowAminaRepo
We will focus in this part on two important libraries for ANN: Tensorflow and Keras. Both of them propose two types of model creation. We will use the high level API of tenshorflow, and the sequential models of Keras.
We will introduce you to some basic important concept related to tensorflow, and we will present you tensorboard. The later one is used to visualize, among other things, quantitative values related to a training process.
[Notebook](https://colab.research.google.com/drive/13KlhoNvYmeRZTZ-TLKAtW3rOkFzQVGYC)
The Impact of Cloud Computing on Predictive Analytics 7-29-09 v5Robert Grossman
This is a talk I gave in San Diego on July 29, 2009 explaining some of the impact and some of the opportunities of cloud computing on predictive analytics.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
DLT UNIT-3.docx
1. DLT UNIT-3
1 (a) What is the Anatomy of a neural network? Explain building
blocks of deep learning?
Training a neural network revolves around
the following objects:
Layers, which are combined into a network (or model)
The input data and corresponding targets
The loss function, which defines the feedback signal used for
learning
The optimizer, which determines how learning proceeds
Layers: the building blocks of DL
A layer is a data-processing module that takes as
input tensors and that outputs tensors.
Different layers are appropriate for different types of data
processing.
Dense layers for 2D tensors (samples, features) - simple vector
data
RNNs (or LSTMs) for 3D tensors (samples, time-steps, features)
- sequence data
2. CNNs for 4D tensors (samples, height, width, colour_depth) -
image data
We can think of layers as the LEGO bricks of deep learning.
Building deep-learning models in Keras is done by clipping
together compatible layers to form useful data-transformation
pipelines.
In Keras, the layers we add to our models are dynamically built
to match the shape of the incoming layer.
from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(32, input_shape=(784,)))
model.add(layers.Dense(32))
The second layer didn’t receive an input shape argument - instead,
it automatically inferred its input shape as being the output shape
of the layer that came before
1(b) List the key features of Keras? Write two options for
running Keras.
Keras is an open-source deep learning framework that is known for its
user-friendliness and versatility. It's built on top of other deep learning
libraries like TensorFlow and Theano, which allows users to easily
create and train neural networks. Here are some key features of Keras:
1. User-Friendly: Keras is designed to be user-friendly and easy
to use. Its high-level API makes it accessible to both beginners
and experienced machine learning practitioners.
2. Modularity: Keras is built with a modular architecture. It
allows users to construct neural networks by stacking layers,
making it easy to design complex network architectures.
3. Support for Multiple Backends: Keras originally supported
multiple backends like TensorFlow, Theano, and Microsoft
Cognitive Toolkit (CNTK). However, since TensorFlow 2.0,
Keras has been integrated as the official high-level API of
TensorFlow, making TensorFlow the default backend.
3. 4. Extensibility: Keras is highly extensible, allowing users to
define custom layers, loss functions, and metrics. This makes it
suitable for research and experimentation.
5. Pre-trained Models: Keras provides access to popular pre-
trained models for tasks like image classification, object
detection, and natural language processing through its
applications module. These pre-trained models can be fine-
tuned for specific tasks.
6. GPU Support: Keras leverages the computational power of
GPUs, which significantly accelerates the training of deep
neural networks.
7. Visualization Tools: Keras includes tools for visualizing model
architectures, training history, and more, making it easier to
understand and debug neural networks.
8. Callback System: Keras offers a callback system that allows
users to specify functions to be executed at various stages during
training. This can be used for tasks like model checkpointing,
early stopping, and custom logging.
9. Integration with Data Libraries: Keras seamlessly integrates
with popular data manipulation libraries like NumPy and data
preprocessing libraries like TensorFlow Data Validation
(TFDV) and TensorFlow Data Validation (TFDV).
Two options for running Keras are:
1. TensorFlow with Keras: As of TensorFlow 2.0 and later,
Keras is included as the official high-level API of TensorFlow.
You can use Keras by simply importing it from TensorFlow and
building your models using the Keras API. For example:
4. 2. Stand-alone Keras with TensorFlow Backend: Before
TensorFlow 2.0, Keras was often used as a standalone library
with TensorFlow as a backend. You can install and use
standalone Keras by installing the Keras package and
configuring it to use TensorFlow as the backend. Here's how
you can do it:
Install Keras: pip install keras
Configure Keras to use TensorFlow backend:
3. You can then build and train your models using the standalone
Keras API as you would with TensorFlow.
Note that, as of TensorFlow 2.0, it's recommended to use Keras
through TensorFlow due to its seamless integration and the fact that
Keras is now the official high-level API of TensorFlow.
5. 2 (a) How to set up the deep learning workstations?
Explain with example.
6. 2 (b) What is hypothesis space and explain the
functionalities of Loss functions and Optimizers?
Hypothesis Space: The hypothesis space, often referred to as the
hypothesis class or model space, is a fundamental concept in machine
learning and statistical modeling. It represents the set of all possible
7. models or functions that a machine learning algorithm can use to
make predictions or approximate a target variable. In simpler terms,
it's the space of all possible solutions that the algorithm considers
when trying to learn from data.
The hypothesis space depends on the choice of machine learning
algorithm and the model architecture. For example:
In linear regression, the hypothesis space includes all possible
linear functions of the input features.
In decision tree algorithms, the hypothesis space includes all
possible binary decision trees that can be constructed from the
features.
In neural networks, the hypothesis space consists of all possible
network architectures with varying numbers of layers and
neurons in each layer.
The goal of training a machine learning model is to search within this
hypothesis space to find the best model that fits the given data and
generalizes well to unseen data. This search is guided by a
combination of loss functions and optimizers.
Loss Functions: A loss function, also known as a cost function or
objective function, quantifies how well a machine learning model's
predictions match the actual target values in the training data. It
essentially measures the "loss" or error between the predicted values
and the true values. The choice of a loss function depends on the type
of machine learning task you're working on:
1. Regression Tasks: In regression problems where the goal is to
predict a continuous value (e.g., predicting house prices),
common loss functions include mean squared error (MSE) and
mean absolute error (MAE). MSE penalizes larger errors more
heavily, while MAE treats all errors equally.
2. Classification Tasks: In classification problems where the goal
is to assign data points to discrete classes or categories (e.g.,
image classification), common loss functions include cross-
entropy loss (log loss) for binary or multi-class classification.
8. 3. Custom Loss Functions: In some cases, you might need to
design custom loss functions to address specific requirements or
challenges in your problem domain.
The optimizer's role is to minimize the loss function by adjusting the
model's parameters during the training process.
Optimizers: Optimizers are algorithms or methods used to update the
model's parameters (e.g., weights and biases in a neural network) in
order to minimize the loss function. They determine how the model
should adjust its parameters to make its predictions more accurate.
Common optimizers include:
1. Gradient Descent: Gradient descent is a fundamental
optimization algorithm that iteratively updates model parameters
in the direction of the steepest decrease in the loss function.
Variants of gradient descent include stochastic gradient descent
(SGD), mini-batch gradient descent, and more advanced
algorithms like Adam and RMSprop.
2. Adaptive Learning Rate Methods: These optimizers
automatically adjust the learning rate during training to speed up
convergence. Examples include Adam, RMSprop, and Adagrad.
3. Constrained Optimization Methods: In some cases,
optimization may need to adhere to certain constraints, such as
L1 or L2 regularization. Algorithms like L-BFGS and Conjugate
Gradient can be used for constrained optimization.
4. Evolutionary Algorithms: In some cases, optimization
problems are solved using evolutionary algorithms like genetic
algorithms and particle swarm optimization.
The choice of optimizer can significantly impact the training speed
and final performance of a machine learning model. It's often
necessary to experiment with different optimizers and
hyperparameters to find the best combination for a specific problem.