- Murat Karakaya Akademi Youtube
- Murat Karakaya Akademi Medium
- Murat Karakaya Akademi Kaggle
- Murat Karakaya Akademi GitHub Pages
I prepare the below tutorials to help you at solving Deep Learning problems with TensorFlow and Keras. You can also watch all the tutorials in English or Turkish at the Murat Karakaya Akademi Youtube channel.
I hope you find these tutorials helpful and useful.
Enjoy!
Murat Karakaya
If you are interested in Seq2Seq Learning, I have a good news for you. Recently, I have been working on Seq2Seq Learning and I decided to prepare a series of tutorials about Seq2Seq Learning from a simple Multi-Layer Perceptron Neural Network model to Encoder Decoder Model with Attention.
- PART A: Introduction & A Simple NN Solution
- Part B: Using LSTM layer in a Recurrent Neural Network
- Part C: Basic Encoder Decoder Model
- Part D: Encoder Decoder with Teacher Forcing
- Part E: Encoder Decoder for Variable Input Output Size with Teacher Forcing
- Part F: Encoder Decoder with Bahdanau & Luong Attention Mechanism
In this series, we will focus on how to Build Efficient TensorFlow Input Pipelines in Deep Learning with Tensorflow & Keras. We will review the tf.data module. Using tf.data.Dataset methods, we will learn how to map, prefetch, cache, and batch the datasets correctly so that the data input pipeline will be efficient in terms of time and performance. We will discuss how map, prefetch, cache, and batch functions affect the performance of the tf.data.Dataset input pipeline performance.
Moreover, we will see how to use TensorBoard add-on "TF Profiler" for monitoring the performance and bottlenecks of the tf.data input pipeline.
- Build an Efficient TensorFlow Input Pipeline for Char-Level Text Generation
- Build an Efficient TensorFlow Input Pipeline for Word-Level Text Generation
- tf data: Build Efficient TensorFlow Input Pipelines for Image Datasets
- How to Evaulate Models in Multi-Label Classification
- How to Evaluate a Classifier Trained with an Imbalanced Dataset? Why Accuracy is not Enough?
- What are Convolution, Filters, and Feature Map? How Keras Conv2d Layer Works?
- Understand and Use Keras Conv1d Layer: Predict House Prices
- How to solve Classification Problems
- How to solve Multi-Class Image Classification Problems
- How to solve Multi-Label Image Classification Problems
- How to solve Binary Image Classification Problems
- How to solve Binary Text Classification Problems
- Multi-Class Text Classification with a GPT3 Transformer block: An End-to-End Example
- Using Keras Text Vectorization Layer For a Multi-Class Text Classification
- How to Evaluate a Classifier Trained with an Imbalanced Dataset? Why Accuracy is not Enough?
- Multi Topic (Multi Class) Text Classification With various Deep Learning Models
- LSTM: Introduction
- LSTM: Understanding Output Types
- LSTM: Understanding the Number of Parameters
- Using LSTM layer in a Recurrent Neural Network Model
- Basic Encoder Decoder Model with LSTM
- Character Level Text Generation with an LSTM Model
In this series, we have been covering all the topics related to Text Generation with sample implementations in Python, Tensorflow & Keras.
- Text Generation: Fundementals
- Build an Efficient TensorFlow Input Pipeline for Char-Level Text Generation
- Build an Efficient TensorFlow Input Pipeline for Word-Level Text Generation
- Text Generation: Sampling
- Character Level Text Generation with an LSTM Model
- Character Level Text Generation with an Encoder Decoder Model
In this series, we have been covering all the topics related to Controllable (Conditioned) Text Generation with sample implementations in Python, Tensorflow & Keras.
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