Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems

Explain the core concepts and components of neural networks and the challenges of training deep networks

Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.

Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling

Create custom layers and models in Keras and integrate Keras with TensorFlow 2.x

Develop advanced convolutional neural networks (CNNs) using Keras

Develop Transformer models for sequential data and time series prediction

Explain key concepts of Unsupervised learning in Keras, Deep Q-networks (DQNs), and reinforcement learning

Get hands-on building, training, and evaluating PyTorch models you can showcase in your professional portfolio

Gain practical experience with tensors, datasets, and automatic differentiation using PyTorch core tools, including autograd and DataLoader

Develop linear regression models using gradient descent, mini-batch optimization, and training/validation splits to evaluate model performance

·Apply cross-entropy loss, sigmoid-based classification, and advanced optimization techniques to build logistic regression models in PyTorch

Get hands-on experience using PyTorch to build and deploy AI systems and complete a portfolio-worthy project.

Develop and train shallow neural networks with various architectures and apply Softmax regression in multi-class classification problems.

Explore deep neural networks, including techniques such as dropout, weight initialization, and batch normalization.

Gain practical experience with convolutional neural networks, exploring layers, activation functions, and more.

Demonstrate your hands-on skills in building deep learning models using Keras and PyTorch to solve real-world image classification problems

Showcase your expertise in designing and implementing a complete deep learning pipeline, including data loading, augmentation, and model validation

Highlight your practical skills in applying CNNs and vision transformers to domain-specific challenges like geospatial land classification

Communicate your project outcomes effectively through a model evaluation

Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models

Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks

Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer

Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets

Explain how one-hot encoding, bag-of-words, embeddings, and embedding bags transform text into numerical features for NLP models

Implement Word2Vec models using CBOW and Skip-gram architectures to generate contextual word embeddings

Develop and train neural network-based language models using statistical N-Grams and feedforward architectures

Build sequence-to-sequence models with encoder–decoder RNNs for tasks such as machine translation and sequence transformation

Explain the role of attention mechanisms in transformer models for capturing contextual relationships in text

Describe the differences in language modeling approaches between decoder-based models like GPT and encoder-based models like BERT

Implement key components of transformer models, including positional encoding, attention mechanisms, and masking, using PyTorch

Apply transformer-based models for real-world NLP tasks, such as text classification and language translation, using PyTorch and Hugging Face tools

Sought-after, job-ready skills businesses need for working with transformer-based LLMs in generative AI engineering

How to perform parameter-efficient fine-tuning (PEFT) using methods like LoRA and QLoRA to optimize model training

How to use pretrained transformer models for language tasks and fine-tune them for specific downstream applications

How to load models, run inference, and train models using the Hugging Face and PyTorch frameworks

In-demand generative AI engineering skills in fine-tuning LLMs that employers are actively seeking

Instruction tuning and reward modeling using Hugging Face, plus understanding LLMs as policies and applying RLHF techniques

Direct preference optimization (DPO) with partition function and Hugging Face, including how to define optimal solutions to DPO problems

Using proximal policy optimization (PPO) with Hugging Face to build scoring functions and tokenize datasets for fine-tuning

In-demand, job-ready skills businesses seek for building AI agents using RAG and LangChain in just 8 hours

How tapply the fundamentals of in-context learning and advanced prompt engineering timprove prompt design

Key LangChain concepts, including tools, components, chat models, chains, and agents

How tbuild AI applications by integrating RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies

Gain practical experience building your own real-world generative AI application to showcase in interviews

Create and configure a vector database to store document embeddings and develop a retriever to fetch relevant segments based on user queries

Set up a simple Gradio interface for user interaction and build a question-answering bot using LangChain and a large language model (LLM)