Artificial Intelligence - All in One

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1.Data Analysis with Python, Introduction to Pandas,1.1 Working with Dataframe 2024.

1.Deep Learning with TensorFlow2,4.Sequence Models 1..20 Classifying Sentences with 1D CNN 2024.

1.Deep Learning with TensorFlow2,4.Sequence Models 1.19 Classifying Sentences with RNN 2024

1.Deep Learning with TensorFlow2,4.Sequence Models 1.18 Encodings and Embeddings 2024.

1.Deep Learning with TensorFlow2,4.Sequence Models 1.17 The Problem of Text Classification 2024.

1.Deep Learning with TensorFlow2 Why DeepLearning Image Representation 1.16 Bringing it All Together

1.Deep Learning with TensorFlow2 Why Deep Learning Image Representation 1.15 Dense Layers 2024.

1.Deep Learning with TensorFlow2 Why Deep Learning Image Representation 1.14 Pooling 2024.

1.Deep Learning with TensorFlow2 Why Deep Learning Image Representation 1.13 Convolution 2024.

1.Deep Learning with TensorFlow2 Why Deep Learning Image Representation 1.12 Image Representation

1.Deep Learning with TensorFlow2 Why Deep Learning1.11 Reviewing Cost Functions 2024.

1.Deep Learning with TensorFlow2 Why Deep Learning1.10 Reviewing Optimizers 2024.

1.Deep Learning with TensorFlow2 Why Deep Learning1.9 Revisiting Activation Functions 2024

1.Deep Learning with TensorFlow2 Why Deep Learning 1.8 Going Deep with Layers and Neurons 2024.

1.Deep Learning with TensorFlow2 Why Deep Learning Limit of Classification with Logistic Regression

1.Deep Learning with TensorFlow2 7 Steps Getting Started 1.6 Building Your First Model 2024.

1.DeepLearning with TensorFlow2 7 Steps Getting Started 1.5 Understanding Tensors and Dimensionality

1.Deep Learning with TensorFlow 2.0 in 7 Steps1.Getting Started 1.4 Getting Started with TensorFlow2

06. Modern NLP Trans and Large Language Models - Conclusion 01 Next steps 2024.

5.Modern NLP Trans and Large Language Models 5.7 Solution Parameter efficient fine tuning with LoRa

5.Modern NLP Trans and Large Language Models 5.6 Challenge Parameter efficient fine tuning with LoRa

5.Modern NLP Transformers and Large Language Models, 5.5 Parameter efficient fine tuning PEFT 2024.

5.Modern NLP Transformers and Large Language Models, 5.4 Retrieval augmented generation RAG 2024.

5.Modern NLP Transformers and Large Language Models, 5.3 Fine tuning methods 2024.

5.Modern NLP Transformers and Large Language Models, 5.2 Supervised fine tuning 2024.

5.Modern NLP Transformers and Large Language Models, 5.1 Methods that improve LLM performance 2024.

4.Modern NLP Transformers and Large Language Models 4.5 Solution Sentiment analysis using DistilBERT

4.Modern NLPTransformers and Large Language Models 4.4 Challenge Sentiment analysis using DistilBERT

4. Modern NLP Transformers and Large Language Models 4.3 Large language models BERT, GPT 2024.

1.Deep Learning with TensorFlow 2.0 in 7 Steps1.Getting Started 1.3 Getting Started with TensorFlow