Learn about TensorFlow 2.0. Main Commands and Operations (compare with TF 1.X)
MLP Mixer Is All You Need? Understanding MLP-Mixers from beginning to the end, with TF Keras code.
How PyTorch Is Challenging TensorFlow Lately. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.
In this tutorial, we'll learn How AI Can Help Us Recycle. An application of Convolutional Neural Networks (CNNs) to waste image-classification.
Can MXNet Stand Up To TensorFlow & PyTorch? MXNet offers powerful tools for developers to exploit the full capabilities of GPUs and cloud computing.
TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera. Get it working! Result: wow, just works, completed in a short time.
MXNet offers powerful tools for developers to exploit the full capabilities of GPUs and cloud computing.
In this video, we are going to build a pre-trained UNET architecture in TensorFlow using Keras API. Here, in this video, we are going to use an EfficientNet trained on the ImageNet dataset as the pre-trained encoder for our famous UNET architecture.
In this video, we're going to discuss the top 10 Python Libraries for Data Science: TensorFlow, NumPy, SciPy, Keras, Pandas, Matplotlib, Scrapy, Scikit-learn, BeautifulSoup, PyTorch. Data science is an extremely important field in current times and Python is one of the best programming languages for Data Science mainly due to its extensive library support for data science and analytics. There are numerous Python libraries that contain a host of functions, tools, and methods to manage, analyze and visualize the data. So, let's get started now.
PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.
Tensorflow tf.Data api allows you to build a data input pipeline. Using this you can handle large dataset for your deep learning training by streaming training samples from hard disk or S3 storage. tf.data.Dataset is the main class in tf.data api. In this video we see how tf pipeline allows not only to stream the data for training but you can peform various transformations easily by writing a single line of code.
How to use Tokenizer and also introduce Padded Sequences tool in NLP with Tensorflow and Keras. Simple examples in Python.
In this video I show you 10 deep learning projects from beginner to advanced that you can do with TensorFlow or PyTorch. I also tell you which datasets you need and where you can find them.
In this episode, I am going to show you- How we can convert PyTorch model into a Tensorflow model. We are going to make use of ONNX[Open Neural Network Exchange] to achieve the model conversion.
From Experimentation to Products: The Production Machine Learning Journey • Robert will discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow.
Class imbalance is a common challenge when training Machine Learning models. In this tutorial, we'll learn Dealing with Imbalanced Data in TensorFlow: Class Weights.
This Edureka Recurrent Neural Networks tutorial video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
In this tutorial, we'll learn 5 Steps to Passing the TensorFlow Developer Certificate. Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework.
A practical example of how to build several Machine Translation models in Python and Tensorflow. It's nothing special. Why is it used by so many professionals? Read this article to the end and you will understand. Let's explore it with us now.
Installing TensorFlow GPU (Updated). Very simple, in just a few steps.