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neural network

Consistency of Bilinear Upsampling Layer


It is well known among deep-learning manias that bilinear upsampling layers in TensorFlow have pixel-offset issues. But the problem remains to cause inconsistent computation flow when exporting a trained model in TensorFlow into another DL framework through various versions. In my case, a neural network model with bilinear upsampling layers showed weird behavior when converting the trained model from TensorFlow 2.5 to Apple Core ML by using coremltools 3.4. After uncountable coding, trials, and delete-delete-delete, I nearly gave up the consistent results of the upsampling layer between TensorFlow and Core ML. I wanted to use Keras in the latest TensorFlow 2.5 for training in Windows PC, and I wanted to use the previous coremltools 3.4 for converting the trained model to Core ML for my macOS laptop.

DataScience Digest -- 17.06.21


The never-ending fight with bias and AI systems that learn by watching YouTube. EU mobilizes to rein in tech giants. Facebook's AI has migrated all their AI systems to PyTorch. Within a year, there are more than 1,700 PyTorch-based inference models in full production at Facebook, and 93 percent of their new training models are on PyTorch. The times are hardly perfect for self-driving car companies.

A guide to the field of Deep Learning


Since the list has gotten rather long, I have included an excerpt above; the full list is at the bottom of this post. At the entry level, the datasets used are small. Often, they easily fit into the main memory. If they don't already come pre-processed then it's only a few lines of code to apply such operations. Mainly you'll do so for the major domains Audio, Image, Time-series, and Text. Before diving into the large field of Deep Learning it's a good choice to study the basic techniques.

Industry Tech Outlook Magazine


The exponential growth of data traffic in our digital age poses some real challenges on processing power. And with the advent of machine learning and AI in, for example, self-driving vehicles and speech recognition, the upward trend is set to continue. All this places a heavy burden on the ability of current computer processors to keep up with demand. Now, an international team of scientists has turned to light to tackle the problem. The researchers developed a new approach and architecture that combines processing and data storage onto a single chip by using light-based, or "photonic" processors, which are shown to surpass conventional electronic chips by processing information much more rapidly and in parallel.

DeepMind scientist calls for ethical AI as Google faces ongoing backlash


Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Raia Hadsell, a research scientist at Google DeepMind, believes "responsible AI is a job for all." That was her thesis during a talk today at the virtual Lesbians Who Tech Pride Summit, where she dove into the issues currently plaguing the field and the actions she feels are required to ensure AI is ethically developed and deployed. "AI is going to change our world in the years to come. But because it is such a powerful technology, we have to be aware of the inherent risks that will come with those benefits, especially those that can lead to bias, harm, or widening social inequity," she said.



This course will teach you foundations of deep learning and TensorFlow as well as prepare you to pass the TensorFlow Developer Certification exam (optional). Videos going through the rest of the notebooks (03 - 10) are available in the full course. New You can now read the full course as an online book! (note: this is a work in progress, but 95% of it should run fine) Check out the livestream Q&A celebrating the course launch on YouTube. Otherwise, many of them might be answered below. This table is the ground truth for course materials.

Using Conditional Deep Convolutional GANs to generate custom faces from text descriptions


GANs (Generative Adversarial Networks) are a subset of unsupervised learning models that utilize two networks along with adversarial training to output "novel" data which resembles the input data. More specifically, GANs typically involve "a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G [1]." Conditional GANs are a modification of the original GAN model, later proposed by Mehdi Mirza and Simon Osindero in the paper, "Conditional Generative Adversarial Nets" (2014). In a cGAN (conditional GAN), the discriminator is given data/label pairs instead of just data, and the generator is given a label in addition to the noise vector, indicating which class the image should belong to. The addition of labels forces the generator to learn multiple representations of different training data classes, allowing for the ability to explicitly control the output of the generator. When training the model, the label is usually combined with the data sample for both the generator and discriminator.

Face Recognition System using DEEPFACE(With Python Codes)


Recognition of the face as an identity is a critical aspect in today's world. Facial identification and recognition find its use in many real-life contexts, whether your identity card, passport, or any other credential of significant importance. It has become quite a popular tool these days to authenticate the identity of an individual. This technology is also being used in various sectors and industries to prevent ID fraud and identity theft. Your smartphone also has a face recognition feature to unlock it.



This article will dive you into the built-in string methods that are used in various text processing tasks in machine learning projects. String methods help to implement sequence operations with the help of these methods. Let's see all the string methods used in the string class of python. First, assign a string to a variable and that variable will be an instance or object of the string class. In this method, the string is return with a first letter capital.