Deep Learning with Python and PyTorch (IBM)

Colleagues, in the “Deep Learning with Python and PyTorch” training from IBM you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods. You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons. You will then learn how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. We will then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You will finally learn about dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, and deep autoencoders, and transfer learning with autoencoders, and autoencoder applications. Learn to Apply knowledge of Deep Neural Networks and related machine learning methods, Build and Train Deep Neural Networks using PyTorch, and Build Deep learning pipelines.

Enroll today (teams & execs welcome): https://edx.sjv.io/rEKyEQ 

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age”  (Audible) (Kindle) 

Much career success, AI Academy (kindly subscribe and share with your team)