Aprenda Fundamentals of Generative AI and Large Language Models: Theory and Practice gratuitamente com 75 vídeo(s). Curso online grátis de IA generativa | YouCourse.
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Lec 74 Tutorial on LLMs, Multimodal LLMs, Bias, Fairness, Evaluation and Safety
BPE, decoding, text generation, visual question answering, multimodal evaluation, exact match, token F1, ROUGE-L, ground…
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Lec 73 Bias, Fairness and Safety
bias, representational harm fairness, subgroup metrics, hallucination, governance, prompt injection.
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Lec 72 Size vs Performance and Benchmarking
model scale, active parameters, mixture-of-experts, MoE, MMLU, BIG-Bench, GSM8K, HumanEval, TruthfulQA, LLM-as-a-judge
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Lec 71 LLMs for Audio, Video and Multimodal Tasks
multimodal LLMs, non-text modality encoder, projector, alignment, multimodal tokens
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lec 70 LLMs for Text Generation
LLMs, tokenizer, tokens, subwords, byte-pair encoding, BPE, greedy decoding, sampling, temperature, top-k, top-p
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Lec 69 Tutorial on In-context Learning, RAG and LoRA
ICL, RAG, LoRA
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Lec 68 RAG Recent Advances
Graph RAG, Agentic RAG
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Lec 67 RAG Principle
Non-parametric memory, retrieval, RAG
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Lec 66 Low-Rank Adaptation (LoRA)
LoRA, PEFT, QLoRA
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Lec 65 In-context Learning
Context, few-shot learning, in-context learning
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Lec 64 LLMs – Recap
Attention, multi-head attention, encoder, decoder, LLMs
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Lec 63 Hands-on on LLM
Hands-on on LLM
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Lec 62 Prompt Engineering Basics
Prompt Engineering,Zero shot prompting
One shot prompting
Few shot prompting
Chain of thoughts, Role prompting
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Lec 61 Generative Pre-trained Transformer(GPT) Decoder-only Large Language Model
GPT
Autoregressive text generation,
Large Language Model
Limitations of GPT
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Lec 60 Bidirectional Encoder Representationsfrom Transformer(BERT)
BERT,Pre-training, fine-tuning
Masked Language Modeling
Next Sentence Prediction
Variants of BERT
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Lec 59 Transformer Architecture: Decoder
Decoder Architecture
Encoder Decoder connection
Subword Tokenization
Byte Pair Encoding (BPE)
WordPiece Tokenization
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Lec 58 Practical Exercise - 1
Hands-on Exercise 1: Text Processing with RNN and LSTM
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Lec 57 Transformer Architecture: Encoder
Transformers,Positional encoding,
Residual Connection and Layer normalization
Position-wise Feed-Forward Network
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Lec 56 Evolution From LSTMs to Transformers
Attention mechnaism,
Encoder Decoder attention,positional encoding
multihead attention, masked self attention
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Lec 55 Sequential Modeling with RNNs and LSTM
Sequence Model,temporal dependency,
Recurrent neural networks, LSTMs
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Lec 54 Foundations of Natural Language Processing
Natural Language processing,Text preprocessing,
Tokenization
Stop Word Removal
Lemmatization
Text Representation
Word2Ve…
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Lec 53 Hands-on on reverse diffusion process
Hands-on on reverse diffusion process
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Lec 52 Stable Diffusion Model
Latent Diffusion,stable diffusion,
cross attention.
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Lec 51 Classifier free Diffusion Models
Classifier free guidance,
sampling process
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Lec 50 Classifier Guided Diffusion Model
Classifier guidance,
guidance scale, sampling equation
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Lec 49 U-Net for Denoising
U-Net, Time embedding, Encoder Block
Bottleneck Block,Decoder Block
Residual Blocks,Self-Attention
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Lec 48 Hand on on forward Diffusion process
Hand on on forward Diffusion process
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Lec 47 Denoising Diffusion Probabilistic Model-Reverse Process
Reverse Diffusion process,
true reverse distribution,
Learned reverse ditribution
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Lec 46 Denoising Diffusion Probabilistic Model-Forward Process
Markov Chain,
Forward Diffusion Process
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Lec 45 Mathematics of Diffusion Models
Gaussian Noise Process,
Markov Chain,Noise Prediction
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Lec 44 Introduction to Diffusion Model
Forward noising process
Reverse denoising process
High-Quality Image Generation
Stable Training
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Lec 43 Practical Exercise - 1
Hands-on Exercise 1: Implementation of Cycle GAN
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Lec 42 Style GAN 2
Artifact-Free Image Synthesis,water droplet artifact,
phase artifactWeight modulation and
Demodulation,
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Lec 41 Style GAN
Style-Based Generator,Latent Space Mapping,
Adaptive Instance Normalization
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Lec 40 Cycle GAN
Unpaired Image Translation,Cycle Consistency Loss,
Dual Generators & Discriminators,Domain Adaptation
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Lec 39 Pix2Pix GAN
Image-to-Image Translation,Paired Dataset,U-Net Generator
PatchGAN Discriminator
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Lec 38 Conditional GAN
Conditional Input,Controlled Generation,
Label Conditioning,Paired Learning
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Lec 37 Practical Exercise - 2
Hands-on Exercise 2: Implementation of DCGAN
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Lec 36 Practical Exercise - 1
Hands-on Exercise 1: Implementation of Basic Vanilla GAN
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Lec 35 Introduction to DCGAN
DCGAN
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Lec 34 GAN Convergence and Nash equilibrium
Nash equilibrium
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Lec 33 GAN Objective and Loss Functions
GAN Objective and Loss Functions
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Lec 32 GAN Architecture
Generator Training - Generator Loss, Backward Pass: Generator
Discriminator Training - Discriminator Loss, Backward Pass…
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Lec 31Motivation for GANs
Limitations of VAE, Motivation for GANs, Adversarial Learning
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Lec 30 Practical Exercise - 3
Hands-on Exercise 3: Implementation of Beta Variational Autoencoder (ßVAE)
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Lec 29 Practical Exercise - 2
Hands-on Exercise 2: Implementation of Conditional Variational Autoencoder (cVAE)
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Lec 28 Latent Space Interpolation
Latent Space Interpolation
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Lec 27 Conditional VAE
c-VAE Architecture, cVAE Loss Function, Numerical Example
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Lec 26 Entanglement, Disentanglement Latent Space, and Beta-VAE
Entanglement, Disentanglement Latent Space, and Beta-VAE
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Lec 25 Practical Exercise - 1
Hands-on Exercise 1: Implementation of Variational Autoencoder (VAE)
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Lec 24 Numerical Example
Numerical Example
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Lec 23 Reparameterization Trick
Reparameterization Trick
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Lec 22 Working of Probabilistic Decoder, ELBO, Loss Function of VAE
Probabilistic Decoder, ELBO, VAE Loss Function
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Lec 21 Introduction to VAE and the working of the Encoder
Generative Modelling, Appropriate Posterior Distribution, Likelihood distribution, Probabilitstic Encoder
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Lec 20 Intuition behind KL Divergence - Part B
Self Information, Entropy, Cross-Entropy
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Lec 19 Intuition behind KL Divergence - Part A
Forward KL, Reverse KL, Mode covering, Mode seeking
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Lec 18 Practical Exercise - 2
Hands-on Exercise 2: Regularization techniques in Autoencoders
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Lec 17 Practical Exercise - 1
Hands-on Exercise 1: Types of Autoencoders
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Lec 16 Numerical Example, Limitations of AE
Numerical Example, Limitations of AE
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Lec 15 Regularization Techniques in AE: Contractive
Contractive Autoencoders
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Lec 14 Regularization Techniques in AE: Sparse
KL Divergence as Sparsity Penalty
Sparse Autoencoders
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Lec 13 Regularization Techniques in AE: Denoising
Denoising woking mechanism, Learning Neighboring Pixel Dependencies
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Lec 12 Types of Autoencoders: Architecture and Code-Level Understanding
CNN Autoencoder
Shallow Autoencoder
Deep Autoencoder
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Lec 11 Training objective: Reconstruction Loss (MSE, Binary Cross-Entropy)
Mathimatical understanding of AE, AE Loss Function (MSE, BCE)
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Lec 10 Introduction to Autoencoder
Key components of Autoencoder, Undercomplete, Overcomplete
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Lec 09 Practical Exercise - 2
Hands-on Exercise 2: Building an ensemble model.
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Lec 08 Practical Exercise - 1
Hands-on Exercise 1: Build a CNN for a real-world dataset
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Lec 07 Introduction to Google Colab
Introduction to Google Colab
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Lec 06 Convolutional Neural Network - Part B
ReLU, Pooling, Flatten Layer, Fully Connected Layer, Keras implementation basics
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Lec 05 Convolutional Neural Network - Part A
Pixel level understanding, Spatial information loss, Convolutional Layer, Padding, Stride, Filters
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Lec 04 Optimizers - Part B
AdaGrad, RMSProp, Adam optimizers
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Lec 03 Optimizers - Part A
Role of Optimizers
Gradient Descent, SGD, Mini batch SGD, SGD with Momentum
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Lec 02 Activation Functions & Loss Functions in Deep Learning
Hidden layer activation functions, Output layer activation functions, Loss functions
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Lec 01 Introduction to Generative AI
Discriminative Learning, Generative Learning, Unsupervised generation, Joint modelling, Conditional generation, Applicat…
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Fundamentals of Generative AI and Large Language Models: Theory and Practice (Intro)
