Python, R, TensorFlow & PyTorch — Career Transformation Guide (2022 v2) (Week #5 — article series: “PyTorch — Training & Certifications”) | by Lawrence Wilson | Oct, 2023

Colleagues, our excerpt this week (#5) examines “PyTorch — Training & Certifications” in the global software development arena. The new Python, R, TensorFlow & PyTorch — Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential — Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities.


Building Your First PyTorch Solution: Learn install PyTorch using pip and conda, and see how to leverage GPU support. Next, you will discover how to hand-craft a linear regression model using a single neuron, by defining the loss function yourself. You will then see how PyTorch optimizers can be used to make this process a lot more seamless. Understand how different activation functions and dropouts can be added to PyTorch neural networks. Finally, you will explore how to build classification models in PyTorch. Then extend the PyTorch base module to implement a custom classifier. Upon completion you will have the skills and knowledge to move on to installing PyTorch from scratch in a new environment and building models leveraging and customizing various PyTorch abstractions. Training modules include: 1) Installing PyTorch on a Local Machine, 2) Understanding Linear Regression with a Single Neuron, 3) Building a Regression Model Using PyTorch, and 3) Building a Classification Model Using PyTorch. {Pluralsight}

Deep Neural Networks with PyTorch: Tensor and Datasets, Differentiation in PyTorch, Simple Datasets, Linear Regression, Gradient Descent, Prediction in One Dimension, PyTorch Linear Regression Training Slope and Bia, Training Parameters in PyTorch, Multiple Input Output Linear Regression, Multiple Output Linear Regression, Linear Classifier, Logistic Regression: Prediction, Bernoulli Distribution and Maximum Likelihood Estimation. {IBM}

Introduction to Machine Learning with PyTorch: Supervised Learning — a common class of methods for model construction (Project: Find Donor for CharityML). Second, Deep Learning — learn the foundations of neural network design and training in PyTorch (Project: Build an Image Classifier). And third, Unsupervised Learning — implement unsupervised learning methods for different kinds of problem domains (Project: Create Customer Segments). {Udacity}

Machine Learning with PyTorch: Open Source Torch Library — machine learning, and for deep learning specifically, are presented with an eye toward their comparison to PyTorch, scikit-learn library, similarity between PyTorch tensors and the arrays in NumPy or other vectorized numeric libraries, clustering with PyTorch, image classifiers. {InformIT}

Download your free Python-R-TensoFlow-PyTorch — Career Transformation Guide (2022 v2).

Recommended Reading: Transformative Innovation” audio & eBook series on Amazon:

1 — The Race for Quantum Computing (Audible) (Kindle)

2 — ChatGPT, Gemini and Llama — The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

3 — ChatGPT — The Era of Generative Conversational AI Has Begun (Audible) (Kindle)

Much career success, Lawrence E. Wilson — Artificial Intelligence Academy (share with your team)

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