In a longitudinal study — with a fancy title of “Scared to death? Generalized anxiety disorder and cardiovascular events

Despite the shortcomings, this is a powerful tool to work around cases where Tensorflow doesn’t still support certain NumPy operations. Moreover, we can pass a TF ND array to functions that expect a NumPy ND array, like matplotlib’s plotting functions.

We also saw how to expand what you can do with NumPy, using auto-differentiation, to create and train a Machine Learning model from scratch, on GPU and using TensorFlow-related speed-up tricks, like compilation and auto-vectorization.

The reasons are manifold ranging from lifestyle to biology. For one, depressed and anxious individuals neglect caring for themselves. Motivation to eat healthily, exercise, or adhere to medications and health checkups dissipates. They also turn to alcohol and smoking as coping mechanisms. As depression and anxiety are always persistent, such behaviours easily become habitual — rendering the heart fragile and vulnerable to failure.

Thoughts or the state of mind have power over the human physiology. Disruptive thinking or experiences of catastrophic life events are emotional stressors that activate the bodily stress systems — the sympathetic nervous system and hypothalamic-pituitary-adrenal (HPA) — like any physical stressors would. These stress systems have regulatory mechanisms to shut them down and do not cause any complications if transiently activated. But prolonged and persistent activation — as evident in depressed and anxious individuals — initiate cascades of detrimental consequences in the body.

Excessive sympathetic (“fight or flight”) activities lead to a lower rate of noradrenaline removal from the heart. This causes irregularities in heart rhythms, blood flow, and blood pressure. As sympathetic and parasympathetic (“rest and digest”) activities work in turns, too much of one suppresses the other. Thus, the central element of the parasympathetic system — the vagus nerve — becomes dampened. As the vagus nerve bridges the heart and diaphragm to the brain, a switched off vagal activity disharmonizes both the heart rate variability and breathing rhythm.

Using the TF implementation of NumPy we can place data and operations on CPUs, GPUs or even TPUs and remote devices. All we need to do is use standard TensorFlow mechanisms, such as the tf.device context manager.

Now we are ready to perform a forward pass through this model, calculate the loss and compute the gradients of the model, which, as we said previously, are not TF tensors, but TF ND arrays:

TensorFlow ND arrays can interoperate with NumPy functions and the other way around. However, the conversion of a TF ND array to and from a NumPy ND array may trigger actual data copies, slashing the performance.

My name is Dimitris Poulopoulos and I’m a machine learning engineer working for Arrikto. I have worked on designing and implementing AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.

This model is a simple two-layer neural network, with a ReLU activation between the two linearities. Its weights (w1, w2, bias) are not TF variables, but TF ND arrays. The predict method simulates a forward pass through the layers of the network.

The last line computes the gradients of the weights of the models, which we are watching with the gradient tape. We could now perform an update, using an optimizer like the Stochastic Gradient Descent.

A hyperactive HPA axis makes the adrenal glands release adrenaline and cortisol uncontrollably, leading to cortisol resistance. This concept is similar to that of insulin resistance wherein cells are no longer responsive to insulin and the body produces extra insulin in response. Elevated circulating levels of cortisol have systems-wide negative consequences, such as inducing glucose intolerance, hyperlipidemia (excessive LDL cholesterol), and accumulation of visceral fats. These push the body into a condition of pre-obesity (or obesity) and pre-diabetes (or diabetes), which compounds to heighten the risk of heart diseases.

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aren’t like you or me. If you have to deal with one or witness one on the news, it’s a good idea to be able to identify their narcissistic behavior, know how to deal with it so that you’re not affected, and how to handle their manipulative ways until you can get them completely out of your life.

TensorFlow’s GradientTape can be used to enable auto-differentiation for TensorFlow NumPy code. GradientTape APIs can also return ND array outputs. To watch this in action, let’s define and instantiate a simple model:

But what about operator precedence? Well if you add a TF ND array with a NumPy ND array, the TF one takes precedence. Also, if you pass a TF ND array to a TensorFlow function, the return type will be a TensorFlow tensor. Thus, by design, TensorFlow data types have higher precedence than those of NumPy: