Deep Learning with TensorFlow (Application of Neural Networks)

Colleagues, the Deep Learning with TensorFlow - Applications of Deep Neural Networks to Machine Learning Tasks program brings machine-learning to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory is whiteboarded to provide an intuitive understanding of Deep Learning’s underlying foundations, i.e., artificial neural networks plus provided Python-based Jupyter notebooks. This intermediate-level program includes over 6 hours of instruction. Learn to Build Deep Learning models in TensorFlow and Keras, Interpret the results of Deep Learning models, Troubleshoot and improve Deep Learning models, Understand the language and fundamentals of artificial neural networks and Build your own Deep Learning project. Skill-based training modules include: 1)  Introduction to Deep Learning - overview of deep learning, its roots in artificial neural networks, and the breadth of transformative applications it produces and builds an introductory neural network, 2) How Deep Learning Works - main families of deep neural networks and their applications, shows you deep learning in action via a web application called the TensorFlow Playground. He introduces the archetypal deep learning data sets, and then you build a deep neural network together to tackle a classic machine vision problem, 3) Convolutional Networks - construct a deep network. This lesson builds upon those theoretical foundations to build more effective deep nets, gain an understanding of convolutional layers and how they can be stacked to solve increasingly complex problems with larger data sets. In order to make sense of the outputs from these sophisticated models, the TensorBoard result-visualization tool is used, 4) Introduction to TensorFlow - leading Deep Learning libraries are compared, and then you get down to business with TensorFlow, the open-source library doing the heavy neural network-lifting underneath Keras and build your own deep learning models in TensorFlow, and 5) Improving Deep Networks - tune model hyperparameters and create a deep learning project as well as outlining resources for further self-study.

Enroll today (individuals & teams welcome): https://tinyurl.com/uvyfnwwf 

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

Supervised Learning - Linear Regression in Python

Colleagues, the Supervised Learning - Linear Regression in Python will equip you to apply Least Squares regression and its assumptions to real world data. Then we'll improve on that algorithm with Penalized Regression and non-parametric Kernel methods Learn to apply algorithms in the following areas: Least squares, Penalized least squares, Non-parametric methods, Model selection and fit on real world applications including Insurance and Healthcare. The curriculum addresses: 1) Introduction to Supervised Linear Regression, 2) Introduction to Machine Learning and Supervised Regression - Introduction to Machine Learning and Supervised Regression, Discuss the overall AI ecosystem and how Machine Learning (ML) is part of that ecosystem. - Understand the 3 different types of algorithms that make up ML - Provide some intuition for why functions and optimizations are important in ML, 3) Machine Learning - Understanding Assumptions and survey the statistical concepts important to understanding Linear Algorithms. - Design of experiments. - Conducting experiments. - Understand the difference between linear and non-linear functions, 4) Least Squares Regression - Ordinary Regression - Develop the simple linear regression algorithm. Understand the basic linear regression assumptions. Learn to identify when assumption violations occur. Understand how to evaluate model output, 5) Least Squares Regression - Multiple Regression - Extend the Least Squares algorithm to multiple dimensions Explore data to understand variable importance Prepare data for multiple regression Optimizing between Bias and Variance, 6) Penalized Regression - L1/L2 Optimization - understand motivation behind penalized regression Optimize L1 Regression (Lasso) parameters Optimize L2 Regression (Ridge) parameters Combine the L1/L2 penalties (Elastic Net) Understand the difference and trade offs between Subset Selection and Shrinkage Optimize hyper-parameters with Cross-Validation, 7) Kernel Methods - Support Vector Machines - Understand theory and motivation behind kernel methods. Derive a basic kernel and use the kernel trick. Build a support vector classifier. Extend to regression with a support vector machine. Optimize parameters with Cross validation and Grid Search, 8) Kernel Methods - Gaussian Process Regression - Understand multivariate distributions and non-parametric regression. Use Bayesian probability with joint probabilities. Develop theory behind Gaussian Process Regression. Optimize kernels and hyper-parameters, and 9) Summary and Real World Applications - Review Supervised Linear Regression topics. Perform Linear regression on real world data.

Enroll today (individuals & teams welcome): https://tinyurl.com/uvjs63tw 

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

Deep Learning Algorithms With TensorFlow 2.0

Colleagues the Deep Learning Algorithms With TensorFlow 2.0 program will train you to build Deep Learning neural networks with TensorFlow 2.0. Deep Learning has become the dominant method for speech recognition (Google Assistant), computer vision (search for "my pictures" on Google Photos), language translation, and even game-related Artificial Intelligence (AlphaGo and DeepMind). Gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. You’ll learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You’ll program a model to classify breast cancer, predict stock market prices, and process text as part of Natural Language Processing (NLP). Grasp what Deep Learning and TensorFlow 2.0 are and what problems they have solved and can solve, Study the various Deep Learning model architectures, Apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification; predicting stock market data for Google; classifying Reuters news topics; and classifying flower species, and Apply your newly-acquired skills to a wide array of real-world scenarios. Skill-based training modules include: 1) Deep Learning Introduction and Environment Setup, 2) Building Your First Neural Network for Tabular Data with TensorFlow 2.0, 3) Convolutional Neural Networks with TensorFlow 2.0, 4) Recurrent Neural Network with TensorFlow, 5) Long Short-Term Memory Networks (LSTM), and 6) Transfer Learning with TensorFlow.

Enroll today (individuals & teams welcome): https://tinyurl.com/yakxdynr 

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

Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Colleagues, the Advanced Predictive Techniques With Scikit-Learn And TensorFlow program will train you in advanced predictive techniques with Scikit-Learn and TensorFlow. Better the performance of predictive models, build more complex models, and improve the quality of your predictive models.Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. Learn to use ensemble algorithms to combine many individual predictors to produce better predictions, apply advanced techniques such as dimensionality reduction to combine features and build better models, evaluate models and choose the optimal hyper-parameters using cross-validation the foundations for working and building models using Neural Networks, and learn different techniques to solve problems that arise when doing Predictive Analytics in the real world. Skill-based training modules include: 1) Ensemble Methods for Regression and Classification, 2) Cross-Validation and Parameter Tuning, 3) Working with Features, 4) Introduction to Artificial Neural Networks and TensorFlow, and Predictive Analytics with TensorFlow and Deep Neural Network.

Enroll today (individuals & teams welcome): https://tinyurl.com/bd6r42dc 

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