We will build a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline will accept English text as input and return the French translation. For our model, we will use an English and French sample of sentences. The data is located in data/small_vocab_en and data/small_vocab_fr. The small_vocab_en file contains English sentences with their French translations in the small_vocab_fr file.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

Recurrent Neural Networks (RNN) initially created in the 1980's are a powerful and robust type of neural network in which output from the previous step are fed as input to the current step. The most important feature of RNN is Hidden state and they have memory which remembers each and every information through time. In Recurrent neural networks, we use the result obtained through the hidden layers to process future input as shown in the diagram below. Keras is a very powerful open source Python library which is runs on top of top of other open source machine libraries like TensorFlow, Theano etc, used for developing and evaluating deep learning models and leverages various optimization techniques. In this project we will train a sequence (math expression) to sequence recurrent neural network to implement/solve simple mathematical operations by creating, training and evaluating a simple recurrent neural network model.

Machine learning (a subset of artificial intelligence) involves the advancement of computer algorithms that evolve and improve over time through learned experience. Because these machines learn through repetition, these models are built on training data or sample sets of data. This training develops in the machine the ability to act without relying on a specific program. The problem is that the data used to build these models can be expensive, and the training models are only effective if they're fed the right quantity and quality of data, neither of which is easy or cheap to obtain. Additionally, the cost of training models is expensive.

Researchers reported a deep learning model sorted images of decorated pottery shards as accurately as (and occasionally more precisely than) four expert archaeologists did. A key piece of an archaeologist's job involves the tedious process of categorizing shards of pottery into subtypes. Ask archaeologists why they have put a fragment into a particular category and it's often difficult for them to say what exactly had led them to that conclusion. "It's kind of like looking at a photograph of Elvis Presley and looking at a photo of an impersonator," said Christian Downum, an anthropology professor at Northern Arizona University. "You know something is off with the impersonator, but it's hard to specify why it's not Elvis."

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

Machine learning is one of the most transformative technologies of this generation, but the tech community only is "scratching the surface" when it comes to what's possible, according to Swami Sivasubramanian, vice president of artificial intelligence and machine learning at Amazon Web Services. Machine learning is transforming everything from the way business is conducted to the way people entertain themselves to the way they get things done in their personal lives. "Entire business processes are being made easier with machine learning," Sivasubramanian said during AWS' Machine Learning Summit last week. Supply chain analysts can have faster and more accurate forecasts. And manufacturers can easily spot defects in products." And the barriers to entry have been significantly lowered, enabling builders to quickly apply ML to their most pressing challenges and biggest opportunities. "ML is improving customer experience, creating more efficiencies in operations and spurring new innovations and discoveries like helping researchers discover new vaccines and enhancing agricultural output with better crop monitoring," Sivasubramanian said. "But we are just scratching the surface about what is possible, and there is so much invention yet to be done.

I'm Michael also known as LearnedVector. If you are just getting started in ML and want to get some intuition behind Recurrent neural networks, this post is for you. You can also watch the video version of this post if you prefer. If you want to get into machine learning, recurrent neural networks are a powerful technique that is important to understand. If you use a smartphone or frequently surf the internet, odd's are you've used applications that leverages RNN's.

In the race to build the underlying technologies that can power the next wave of AI revolution, a Chinese lab just toppled OpenAI, the venerated US-based research lab, in terms of who can train a gigantic deep learning model with the most training parameters--as for whether or not there is a race, at least ranking members of the lab believe so. The Beijing Academy of Artificial Intelligence, styled as BAAI and known in Chinese as 北京智源人工智能研究院, launched the latest version of Wudao 悟道, a pre-trained deep learning model that the lab dubbed as "China's first," and "the world's largest ever," with a whopping 1.75 trillion parameters. Unlike conventional deep learning models that are usually task-specific, Wudao is a multi-modal model trained to tackle both text and image, two dramatically different sets of problems. At BAAI's annual academic conference on Tuesday, the institution demonstrated Wudao performing tasks such as natural language processing, text generation, image recognition, image generation, etc. The model is capable of writing poems and couplets in the traditional Chinese styles, answer questions, write essays, generate alt text for images, and generate corresponding images from natural language description with a decent level of photorealism. It is even able to power "virtual idols", with the help of XiaoIce, a Chinese company spun off of Microsoft--so there can be voice support too, in addition to text and image.

ISRO has invited applications from interested candidates for a free online course called "Machine learning to Deep Learning: A journey for remote sensing data classification". The short course can be completed in five days, and participants will also receive a certificate. The course will be conducted through the Indian Institute of Remote Sensing (IIRS) as part of its outreach program. The program coordinator is Dr. Poonam S Tiwari who is from the IIRS DLP team. The ISRO free online course will be conducted from 5 to 9 July in the evening from 4 to 5:30 PM.