Deep Learning for Natural Language Processing - Applications of Deep Neural Networks to Machine Learning Tasks

Colleagues, the Deep Learning for Natural Language Processing  - Applications of Deep Neural Networks to Machine Learning program bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow. Training modules include:  1) The Power and Elegance of Deep Learning for NLP -  linguistics section that introduces the elements of natural language and breaks down how these elements are represented both by Deep Learning and by traditional machine learning approaches. This is followed up with a tantalizing overview of the broad natural language applications in which Deep Learning has emerged as state-of-the-art. The lesson then reviews how to run the code in these LiveLessons on your own machine, as well as the foundational Deep Learning theory that is essential for building an NLP specialization upon. The lesson wraps up by taking a sneak peek at the capabilities you’ll develop over the course of all five lessons, 2) Word Vectors - word vectors are as well as how the beautiful word2vec algorithm creates them. Subsequently, the lesson arms you with a rich set of natural language data sets that you can train powerful Deep Learning models, and then swiftly moves along to leveraging those data to generate word vectors of your own, 3) Modeling Natural Language Data - calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic. Calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents, and add convolutional layers into your deep neural network as well, 4) Recurrent Neural Networks - essential RNN theory, a Deep Learning family that’s ideally suited to handling data that occur in a sequence like languages, apply this theory by incorporating an RNN into your document classification model, and high-level theoretical overview of especially powerful RNN variants—the Long Short-Term Memory Unit and the Gated Recurrent Unit,before incorporating these into your Deep Learning models as well, and 5) Advanced Models - LSTM, namely the Bi-Directional and Stacked varieties. - non-sequential network architectures—where instead of only stacking neural layers on top of each other as we’ve always done and run layers side-by-side in parallel.

Enroll today (eams & execs welcome):  https://tinyurl.com/yks4wu2h 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Deep Learning (Training)

Colleagues, the Deep Learning training program will equip you to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website. Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks. Skill-based training modules include: 1) Neural Networks - build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data., 2) Convolutional Neural Networks - build and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising., 3) Recurrent Neural Networks - build RNNs and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts, 4) Generative Adversarial Network - use GANs to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs, and 5) Deploying a Sentiment Analysis Model - train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new user input. Build a model, deploy it, and create a gateway for accessing it from a website (Project: Deploying a Sentiment Analysis Model). Knowledge of Python, NumPy, pandas, calculus and linear algebra is recommended.

Enroll today (teams & execs welcome): https://tinyurl.com/yj8zehht 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Machine Learning Engineer for Microsoft Azure ($131k average salary)

Colleagues, the Machine Learning Engineer for Microsoft Azure training program will strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning. Learn by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gaining practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom. According to Indeed.com average base salaries for Machine  Learning Engineers is $131,099. Prior experience with Python, Machine Learning, and Statistics is recommended. Skill-based training modules - each with hands-on labs - include: 1) Using Azure Machine Learning - learn to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure (Project: Optimizing and ML Pipeline in Azure); 2) Machine Learning Operations - key concepts of operationalizing machine learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production (Project: Operationalizing Machine Learning); and 3) Capstone Project - use Azure’s Automated ML and HyperDrive to solve a task. Finally, you will have to deploy the model as a web service and test the model endpoint (Project: Training and Building a Machine Learning model in Microsoft Azure).

Enroll today (eams & execs welcome): https://tinyurl.com/2p98sk7a 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Sequence Models for Natural Language Processing (Google Cloud)

Colleagues, the Sequence Models for Natural Language Processing program from Google Cloud is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. Acquire high-demand skills to Predict future values of a time-series, Classify free form text, Address time-series and text problems with recurrent neural networks, Choose between RNNs/LSTMs and simpler models, and Train and reuse word embeddings in text problems. You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow. Training modules address: 1) Working with Sequences - what a sequence is, see how you can prepare sequence data for modeling, and be introduced to some classical approaches to sequence modeling and practice applying them, 2) Recurrent Neural Networks - how they address the variable-length sequence problem, explain how our traditional optimization procedure applies to RNNs, and review the limits of what RNNs can and cannot represent, 3) Dealing with Longer Sequences - learn about LSTMs, Deep RNNs, working with real world data, 4) Text Classification - examine different ways of working with text and how to create your own text classification models, 5) Reusable Embeddings - labeled data for our classification models is expensive and precious. Here we will address how we can reuse pre-trained embeddings to make our models with TensorFlow Hub, 6) Encoder-Decoder Models - sequence-to-sequence model called the encoder-decoder network to solve tasks, such as Machine Translation, Text Summarization and Question Answering, and 6) Summary - review what you have learned so far about sequence modeling for time-series and natural language data.

Enroll today (eams & execs welcome): https://tinyurl.com/2p9yyx3v 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Convolutional Neural Networks in TensorFlow

Colleagues, join the 109k developers enrolled in the Convolutional Neural Networks in TensorFlow training program.  If you want to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Gain high demand skills in Inductive Transfer, Augmentation, Dropouts, Machine Learning and Tensorflow. Training modules include: 1) Exploring a Large Dataset - basic image classification, and you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification, 2) Augmentation: A technique to avoid overfitting - namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers, 3) Transfer Learning - where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario, 4) Multiclass Classifications - when moving beyond binary into Categorical classification there are some coding considerations you need to take into account.

Enroll today (teams & execs welcome): https://tinyurl.com/yckpmbjh 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

TensorFlow Developer Professional Certificate

Colleagues join over 140k professionals enrolled in the TensorFlow Developer Professional Certificate program. According to ZipRecruiter the average TensorFlow Developer in the US earns $148,508 per year. Build applied machine learning skills with TensorFlow so you can build and train powerful models. Learn best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications, handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout, build natural language processing systems using TensorFlow, and apply RNNs, GRUs, and LSTMs as you train them using text repositories. Acquire high demand skills in Computer Vision, Convolutional Neural Network, Machine Learning, Natural Language Processing, Tensorflow, Inductive Transfer, Augmentation, Dropouts, Tokenization, RNNs, Forecasting and Time Series.  Training modules designed to help you pass the certification exam include: 1) Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, 2) Convolutional Neural Networks in TensorFlow, 3) Natural Language Processing in TensorFlow, 4) Sequences, Time Series and Prediction, and 5) Sequences, Time Series and Prediction.

Enroll today (teams & execs welcome): https://tinyurl.com/yc2r2sew 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Data Structures and Algorithms

Colleagues, in the Data Structures and Algorithms program you will learn data structures and algorithms by solving 80+ practice problems. You will begin each course by learning to solve defined problems related to a particular data structure and algorithm. By the end of each course, you would be able to evaluate and assess different data structures and algorithms for any open-ended problem and implement a solution based on your design choices. Knowledge of Python and Basic Algebra is recommended. Skill-based training modules include: 1) Introduction - meet your instructors, and refresh your Python skills. Learn the framework to deconstruct any open-ended problem and then understand the concepts of time and space complexity (Project: Unscramble ComputerScience Problems), 2) Data Structures - can be used to store data. Implement different methods used to manipulate these data structures and examine the efficiency. Understand the advantages and applications of different data structures. Learn how to approach open ended problems (either in interview or real-world) and select appropriate data structures based on requirements (Project: Show Me the Data Structures, 3) Basic Algorithms - such as searching and sorting on different data structures and examine the efficiency of these algorithms. Use recursion to implement these algorithms and then learn how some of these algorithms can be implemented without recursion. Practice selecting and modifying these algorithms for a variety of interview problems (Project: Problems vs. Algorithms), and 4) Advanced Algorithms - such as brute-force greedy algorithms, graph algorithms, and dynamic programming which optimizes recursion by storing results to sub problems. (Project: Route Planner).

Enroll today (teams & execs welcome): https://tinyurl.com/3ua6b24c 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Deep Learning (Training)

Colleagues, the Deep Learning program will equip you to drive advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website. Working knowledge of Python, NumPy, pandas and familiarity with calculus and linear algebra is recommended. Training modules with hands-on projects include: 1) Introduction - apply style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks, 2) Neural Networks - build your first network with Python and NumPy (Project: Predicting Bike-Sharing Patterns), 3) Convolutional Neural Networks - build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them (Project: Landmark Classification & Tagging for Social Media), 4) Recurrent Neural Networks - build your own recurrent networks and long short-term memory networks with PyTorch (Project: Generate TV Scripts), 5) Generative Adversarial Networks - implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs (Project: Generate Faces), and 6) Deploy a Sentiment Analysis Model - deploy a PyTorch sentiment analysis model (Project: Deploying a Sentiment Analysis Model).

Enroll today (teams & execs welcome): https://tinyurl.com/2p8n3na6 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Natural Language Processing in TensorFlow

Colleagues join over 100k professionals enrolled in the Natural Language Processing in TensorFlow training program. teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work and learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an  LSTM on existing text to create original poetry. Build natural language processing systems using TensorFlow, Process text, including tokenization and representing sentences as vectors, Apply RNNs, GRUs, and LSTMs in TensorFlow, and Train LSTM. Gain skills in Natural Language Processing, Tokenization, Machine Learning, Tensorflow and RNNs. Training modules include: 1) Sentiment in text - the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks, 2) Word Embeddings - where tokens are mapped as vectors in a high dimension space. With Embeddings and labeled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space, 3) Sequence models - learn a variety of model formats that are used in training models to understand context in sequence, and 4) Sequence models and literature - use your knowledge for prediction. Given a body of words, you could predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs.

Enroll today (eams & execs welcome): https://tinyurl.com/4wryjdbm 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Deep Learning for Natural Language Processing

Colleagues, the Deep Learning for Natural Language Processing program equips you to build natural language models with deep learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow 2, the most popular Deep Learning library. In early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data. Skill-based lessons include: 1) The Power and Elegance of Deep Learning for NLP - how it has been revolutionized in recent years by Deep Learning approaches, run the code, and theory that is essential for building an NLP specialization upon; 2) Word Vectors - linguistics section that introduces computational representations of natural language elements and an illustrating what word vectors are as well as how the beautiful word2vec algorithm creates them; 3) Modeling Natural Language Data - vector-space embeddings and creating word vectors with word2vec. Whiteboard how to calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic then calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents. The lesson then goes a step further by showing you how to add convolutional layers into your deep neural network as well; 4) Recurrent Neural Networks - theory, apply this theory by incorporating an RNN into your document classification model. Jon then provides a high-level theoretical overview of especially powerful RNN variants--the Long Short-Term Memory Unit and the Gated Recurrent Unit--then incorporate these variants into your deep learning models; and 5) Advanced Models - LSTM  special cases, namely the Bi-Directional and Stacked varieties plus data sets that you can use to train powerful Deep Learning models and other advanced approaches, including sequence generation, seq2seq models, attention, transfer learning, non-sequential network architectures, and financial time series applications.

Enroll today (eams & execs welcome): https://tinyurl.com/4dp57tck 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy