DEEP LEARNING TUTORIALS BY MURAT KARAKAYA AKADEMI

ML_tutorials

DEEP LEARNING TUTORIALS BY MURAT KARAKAYA AKADEMI

TUTORIALS

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

HUGGING FACE TRANSFORMERS

SEQ2SEQ LEARNING

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.

TENSORFLOW DATA PIPELINE

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.

TIME SERIES ANALYSIS

MODEL EVALUATION & METRICS

CONVOLUTIONAL NEURAL NETWORKS (CNN) & KERAS CONVOLUTIONAL LAYERS

CLASSIFICATION IN DEEP LEARNING WITH TENSORFLOW & KERAS

LSTM IN KERAS

TEXT GENERATION WITH KERAS

In this series, we have been covering all the topics related to Text Generation with sample implementations in Python, Tensorflow & Keras.

CONTROLLABLE TEXT GENERATION WITH TRANSFORMERS

In this series, we have been covering all the topics related to Controllable (Conditioned) Text Generation with sample implementations in Python, Tensorflow & Keras.

WEB SCRAPING

TensorFlow & Keras Layers

Large Language Models (LLMs)