#Lossfunctions

[healncureglenview]

Ozempic for Weight Loss in Chicago: A Functional Medicine Approach

If you’re in Chicago and considering Ozempic for weight loss, you might be interested in exploring the approach taken by functional medicine doctors. This holistic perspective prioritizes addressing the root causes of weight gain, while leveraging Ozempic as a potential tool within a broader treatment plan.

What is Ozempic?Ozempic is a prescription medication, aGLP-2 (glucagon-like peptide-2) receptor agonist, primarily used for treating type 2 diabetes. However, it has also shown significant potential for weight loss in individuals with obesity or overweight. It works by mimicking the effects of gut hormones that regulate appetite, satiety, and blood sugar control. Functional Medicine and Weight LossFunctional medicine doctors take a comprehensive approach to weight loss, focusing on identifying and addressing the underlying factors contributing to excess weight. This can include: Hormonal imbalances: Thyroid dysfunction, insulin resistance, and sex hormone imbalances can all impact metabolism and weight. Chronic inflammation: Low-grade inflammation, often linked to dietary choices and stress, can hinder weight loss efforts. Gut health imbalances: Dysbiosis, an imbalance of gut bacteria, can affect nutrient absorption and metabolism. Nutritional deficiencies: Deficiencies in essential vitamins and minerals can disrupt metabolic processes and hinder weight loss.

By addressing these underlying issues, functional medicine doctors aim to create a foundation for sustainable weight loss and improved overall health. Ozempic in Functional Medicine for Weight Loss:While Ozempic can be a valuable tool for weight loss, functional medicine doctors typically view it as part of a holistic approach, not a standalone solution. They may: Combine Ozempic with lifestyle modifications: This includes personalized dietary changes, stress management techniques, and exercise plans to address contributing factors and promote healthy habits. Prioritize gut health: Probiotics, prebiotics, and dietary modifications may be recommended to optimize gut function and support weight loss efforts. Address hormonal imbalances: Functional medicine doctors may utilize natural therapies or medication adjustments to address hormonal imbalances that could be hindering weight loss. Monitor progress and adjust the plan: Regular follow-up appointments allow for personalized adjustments to the treatment plan based on individual needs and progress.

Meena Malhotra, M.D., is the medical director and owner of Heal n Cure, 2420 Ravine Way, Ste. 400, in Glenview. She is an expert in Functional Medicine and Integrative Medicine and serves Glenview and adjoining areas of Wilmette, Winnetka, Lake Forest, Highland Park and Glencoe.Book Discovery Call at https://healncure.com/integrative-functional-medicine-doctor-glenview

[humanengineers]

MIT Introduction to Deep Learning | 6.S191 Source | YouTube | Alexander Amini   https://human-engineers.com/wp-content/uploads/2020/02/HR-V2-11.jpg https://human-engineers.com/mit-introduction-to-deep-learning-6-s191/?feed_id=13419&_unique_id=611e4629e9e20 https://human-engineers.com/mit-introduction-to-deep-learning-6-s191/?feed_id=13419&_unique_id=611e4629e9e20

[artificialintelligence001]

[learnskill321]

[incegna]

Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast and easy to use. Keras doesn't handle low-level computation. Instead, it uses another library to do it, called the "Backend. So Keras is a high-level API wrapper for the low-level API, capable of running on top of TensorFlow, CNTK, or Theano. https://www.incegna.com/post/keras-basic-neural-network Check our Info : www.incegna.com Reg Link for Programs : http://www.incegna.com/contact-us Follow us on Facebook : www.facebook.com/INCEGNA/? Follow us on Instagram : https://www.instagram.com/_incegna/ For Queries : enquiry@incegna.com #keras,#theano,#tensorflow,#backend,#cntk,#python,#API,#neuralnetworks,#cnn,#rnn,#pytorch,#pandas,#numpy,#lossfunction,#metrics,#deeplearning,#artificialneuralnetworks https://www.instagram.com/p/B9qddulg6bY/?igshid=zakyv8w7hdds

[womaneng]

If I had 8 hours to build a machine learning model, I’d spend the first 6 hours preparing my dataset.

- Abraham Lossfunction

[innrpin]

In building Machine Learning models we may have many features in a dataset but all may not be needed so we need to select those features which are really important. This entire process is termed as Feature Engineering. This makes the training faster and also makes the results easy to interpret.

The aim of the process of making a machine learn is to make the model perfectly fit to perform. Hence, there is this possibility of the model not fitting to perfection. The resultant model could be either Underfitting or Overfitting. In other words the machine is to learn what it is intended to learn.

Machines learn by means of a Loss Function. It’s a method of evaluating how well specific algorithm models the given data set. A Loss Function is a measure of how good the prediction model does in terms of being able to predict the expected outcome.

These are measures of errors in the Machine Learning model. Entropy is a measure of the uncertainty associated with a given distribution. Cross-Entropy is also related to and often confused with logistic loss, known as log loss.

#ML,#MachineLearning,#ConfusionMatrix,#FeatureEngineering,#LossFunction,#Hyperplane,#Overfitting,#Underfitting,#50KeyMLConcepts,#makeupandbreakup,#AI,#ArtificialIntelligence

[tak4hir0]

The following problems appeared in the assignments in the Udacity course Deep Learning (by Google). The descriptions of the problems are taken from the assignments (continued from the last post). Classifying the letters with notMNIST dataset with Deep Network Here is how some sample images from the dataset look like: Let’s try to get the best performance using a multi-layer model! (The best reported test accuracy using a deep network is  97.1%). One avenue you can explore is to add multiple layers. Another one is to use learning rate decay.   Learning L2-Regularized  Deep Neural Network with SGD The following figure recapitulates the neural network with a 3 hidden layers, the first one with 2048 nodes,  the second one with 512 nodes and the third one with with 128nodes, each one with Relu intermediate outputs. The L2 regularizations applied on the lossfunction for the weights learnt at the input and the hidden layers are λ1, λ2, λ3 and λ4, respectively.   The next 3 animations visualize the weights learnt for 400 randomly selected nodes from hidden layer 1 (out of 2096 nodes), then another 400 randomly selected nodes from hidden layer 2 (out of 512 nodes) and finally at all 128 nodes from hidden layer 3, at different steps using SGD and L2 regularized loss function (with λ1 = λ2 = λ3 = λ4 =0.01).  As can be seen below, the weights learnt are gradually capturing (as the SGD steps increase) the different features of the letters at the corresponding output neurons.     Results with SGD Initialized Validation accuracy: 27.6% Minibatch loss at step 0: 4.638808 Minibatch accuracy: 7.8% Validation accuracy: 27.6% Validation accuracy: 86.3% Minibatch loss at step 500: 1.906724 Minibatch accuracy: 86.7% Validation accuracy: 86.3% Validation accuracy: 86.9% Minibatch loss at step 1000: 1.333355 Minibatch accuracy: 87.5% Validation accuracy: 86.9% Validation accuracy: 87.3% Minibatch loss at step 1500: 1.056811 Minibatch accuracy: 84.4% Validation accuracy: 87.3% Validation accuracy: 87.5% Minibatch loss at step 2000: 0.633034 Minibatch accuracy: 93.8% Validation accuracy: 87.5% Validation accuracy: 87.5% Minibatch loss at step 2500: 0.696114 Minibatch accuracy: 85.2% Validation accuracy: 87.5% Validation accuracy: 88.3% Minibatch loss at step 3000: 0.737464 Minibatch accuracy: 86.7% Validation accuracy: 88.3% Test accuracy: 93.6%   Batch size = 128 and Drop-out rate = 0.8 for training dataset are used for the above set of experiments, with learning decay. We can play with the hyper-parameters to get better test accuracy. Convolution Neural Network Previously  we trained fully connected networks to classify notMNIST characters. The goal of this assignment is to make the neural network convolutional. Let’s build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we’ll limit its depth and number of fully connected nodes. The below figure shows the simplified architecture of the convolution neural net. As shown above, the ConvNet uses: 5×5 kernels 16 filters 2×2 strides  SAME padding 64 hidden nodes Results Initialized Minibatch loss at step 0: 3.548937 Minibatch accuracy: 18.8% Validation accuracy: 10.0% Minibatch loss at step 50: 1.781176 Minibatch accuracy: 43.8% Validation accuracy: 64.7% Minibatch loss at step 100: 0.882739 Minibatch accuracy: 75.0% Validation accuracy: 69.5% Minibatch loss at step 150: 0.980598 Minibatch accuracy: 62.5% Validation accuracy: 74.5% Minibatch loss at step 200: 0.794144 Minibatch accuracy: 81.2% Validation accuracy: 77.6% Minibatch loss at step 250: 1.191971 Minibatch accuracy: 62.5% Validation accuracy: 79.1% Minibatch loss at step 300: 0.441911 Minibatch accuracy: 87.5% Validation accuracy: 80.5% … … … Minibatch loss at step 900: 0.415935 Minibatch accuracy: 87.5% Validation accuracy: 83.9% Minibatch loss at step 950: 0.290436 Minibatch accuracy: 93.8% Validation accuracy: 84.0% Minibatch loss at step 1000: 0.400648 Minibatch accuracy: 87.5% Validation accuracy: 84.0% Test accuracy: 90.3% The following figures visualize the feature representations at different layers for the first 16 images during training: Convolution Neural Network with Max Pooling The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides by a max pooling operation of stride 2 and kernel size 2. The below figure shows the simplified architecture of the convolution neural net with MAX Pooling layers. As shown above, the ConvNet uses: 5×5 kernels 16 filters 1×1 strides 2×2 Max-pooling  SAME padding 64 hidden nodes Results Initialized Minibatch loss at step 0: 4.934033 Minibatch accuracy: 6.2% Validation accuracy: 8.9% Minibatch loss at step 50: 2.305100 Minibatch accuracy: 6.2% Validation accuracy: 11.7% Minibatch loss at step 100: 2.319777 Minibatch accuracy: 0.0% Validation accuracy: 14.8% Minibatch loss at step 150: 2.285996 Minibatch accuracy: 18.8% Validation accuracy: 11.5% Minibatch loss at step 200: 1.988467 Minibatch accuracy: 25.0% Validation accuracy: 22.9% Minibatch loss at step 250: 2.196230 Minibatch accuracy: 12.5% Validation accuracy: 27.8% Minibatch loss at step 300: 0.902828 Minibatch accuracy: 68.8% Validation accuracy: 55.4% Minibatch loss at step 350: 1.078835 Minibatch accuracy: 62.5% Validation accuracy: 70.1% Minibatch loss at step 400: 1.749521 Minibatch accuracy: 62.5% Validation accuracy: 70.3% Minibatch loss at step 450: 0.896893 Minibatch accuracy: 75.0% Validation accuracy: 79.5% Minibatch loss at step 500: 0.610678 Minibatch accuracy: 81.2% Validation accuracy: 79.5% Minibatch loss at step 550: 0.212040 Minibatch accuracy: 93.8% Validation accuracy: 81.0% Minibatch loss at step 600: 0.785649 Minibatch accuracy: 75.0% Validation accuracy: 81.8% Minibatch loss at step 650: 0.775520 Minibatch accuracy: 68.8% Validation accuracy: 82.2% Minibatch loss at step 700: 0.322183 Minibatch accuracy: 93.8% Validation accuracy: 81.8% Minibatch loss at step 750: 0.213779 Minibatch accuracy: 100.0% Validation accuracy: 82.9% Minibatch loss at step 800: 0.795744 Minibatch accuracy: 62.5% Validation accuracy: 83.7% Minibatch loss at step 850: 0.767435 Minibatch accuracy: 87.5% Validation accuracy: 81.7% Minibatch loss at step 900: 0.354712 Minibatch accuracy: 87.5% Validation accuracy: 83.8% Minibatch loss at step 950: 0.293992 Minibatch accuracy: 93.8% Validation accuracy: 84.3% Minibatch loss at step 1000: 0.384624 Minibatch accuracy: 87.5% Validation accuracy: 84.2% Test accuracy: 90.5% As can be seen from the above results, with MAX POOLING, the test accuracy increased slightly. The following figures visualize the feature representations at different layers for the first 16 images during training with Max Pooling: Till now the convnets we have tried are small enough and we did not obtain high enough accuracy on the test dataset. Next we shall make our convnet deep to increase the test accuracy. To be continued…

[humanengineers]

MIT Introduction to Deep Learning | 6.S191 Source | YouTube | Alexander Amini   https://humanengineers.com/wp-content/uploads/2020/02/HR-V2-11.jpg https://tinyurl.com/yz7apz9b https://humanengineers.com/mit-introduction-to-deep-learning-6-s191/?feed_id=15322&_unique_id=611e416f1b9e5

[artificialintelligence001]

https://insideaiml.com/blog/LossFunctions-in-Deep-Learning-1025

[incegna]

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Check our Info : www.incegna.com Reg Link for Programs : http://www.incegna.com/contact-us Follow us on Facebook : www.facebook.com/INCEGNA/? Follow us on Instagram : https://www.instagram.com/_incegna/ For Queries : enquiry@incegna.com #neuralnetwork,#deeplearning,#neurons,#Python,#Feedforward,#Lossfunction,#Backpropagation,#Datapreparation,#pyplot,#matplotlib,#machinelearning,#ai,#datascience https://www.instagram.com/p/B8vL77eA8cW/?igshid=1i93nyjcyhf4l