DEEP LEARNING MASTER COURSE Germany
Deep learning Training Germany | Deep learning Master Course Germany| Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence. In this course we will start with deep learning introduction and you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning. Mildaintrainings provides The Best Deep Learning Course by Industry Experts.
4 Days / 32 Hrs
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Deep Learning Master Course Germany
Price: USD 460 / INR 30,000 + GST
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Delivery Options: Attend remote-live or on-demand online classes.
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best deep learning course
Master Deep Learning Course Germany
Master Deep Learning Course
Master Deep Learning Training Germany
Deep learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
Objectives Deep Learning course?
- Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
- Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
- Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
- Build deep learning models in TensorFlow and interpret the results
- Understand the language and fundamental concepts of artificial neural networks
- Troubleshoot and improve deep learning models
- Build your own deep learning project
- Differentiate between machine learning, deep learning and artificial intelligence
Who should take this Deep Learning Course?
- Software engineers
- Data scientists
- Data analysts
- Statisticians with an interest in deep learning
Deep Learning Course Germany
Deep Learning Course Germany
1: Introduction to deep learning and a quick recap of machine learning concepts
Introduction to deep learning
- What is a neural network?
- Supervised Learning with Neural Networks
- Why is Deep Learning taking off?
- Introduction to deep learning
2: Building a simple multi-class classification model using logistic regression
Neural Networks Basics
- Binary Classification
- Logistic Regression
- Logistic Regression Cost Function
- Gradient Descent
- Computation graph
- Derivatives with a Computation Graph
- Logistic Regression Gradient Descent
- Vectorizing Logistic Regression
- Vectorizing Logistic Regression’s Gradient Output
- Broadcasting in Python
- A note on python/numpy vectors
- Quick tour of Jupyter/iPython Notebooks
- Explanation of logistic regression cost function (optional)
Shallow neural networks
- Neural Networks Overview
- Deep Learning Honor Code
- Logistic Regression with a Neural Network mindset
- Neural Network Representation
- Computing a Neural Network’s Output
- Vectorizing across multiple examples
- Explanation for Vectorized Implementation
- Activation functions
- Why do you need non-linear activation functions?
- Derivatives of activation functions
- Gradient descent for Neural Networks
- Backpropagation intuition (optional)
- Random Initialization
3: Detecting digits in hand-written digit image, starting by a simple end-to-end model, to a deep neural network
Deep Neural Networks
- Deep L-layer neural network
- Forward Propagation in a Deep Network
- Getting your matrix dimensions right
- Why deep representations?
- Building blocks of deep neural networks
- Forward and Backward Propagation
- Parameters vs. Hyperparameters
- What does this have to do with the brain?
- Deep Neural Network – Application
Key concepts on Deep Neural Networks
- Building your Deep Neural Network: Step by Step
- Deep Neural Network – Application
4: Improving the hand-written digit recognition with convolutional network
Foundations of Convolutional Neural Networks
- Computer Vision
- Edge Detection
- Strided Convolutions
- Convolutions Over Volume
- One Layer of a Convolutional Network
- Simple Convolutional Network Example
- Pooling Layers
- Why Convolutions?
Deep convolutional models: case studies
- Object Localization
- Landmark Detection
- Object Detection
- Convolutional Implementation of Sliding Windows
- Bounding Box Predictions
- Intersection Over Union
- Non-max Suppression
- Anchor Boxes
- YOLO Algorithm
• Special applications: Face recognition & Neural style transfer
- What is face recognition?
- Siamese Network
- Triplet Loss
- Face Verification and Binary Classification
- What is neural style transfer?
- What are deep ConvNets learning?
- Cost Function
- Content Cost Function
- Style Cost Function
- 1D and 3D Generalizations
- Face Recognition for the Happy House
5: Building a model to forecast time data using a recurrent network
Recurrent Neural Networks
- Why sequence models
- Recurrent Neural Network Model
- Back propagation through time
- Different types of RNNs
- Language model and sequence generation
- Sampling novel sequences
- Vanishing gradients with RNNs
- Gated Recurrent Unit (GRU)
- Long Short Term Memory (LSTM)
- Bidirectional RNN
- Deep RNNs
- Building a recurrent neural network – step by step
- Dinosaur Island – Character-Level Language Modeling
- Other-Jazz improvisation with LSTM
Natural Language Processing & Word Embeddings
- Word Representation
- Using word embeddings
- Properties of word embeddings
- Embedding matrix
- Learning word embeddings
- Negative Sampling
- GloVe word vectors
- Sentiment Classification
- Debiasing word embeddings
- Operations on word vectors – Debiasing
Sequence models & Attention mechanism
- Basic Models
- Picking the most likely sentence
- Beam Search
- Refinements to Beam Search
- Error analysis in beam search
- Bleu Score (optional)
- Attention Model Intuition
- Attention Model
- Speech recognition
- Trigger Word Detection
- Neural Machine Translation with Attention
- Trigger word detection
FAQ | Master Deep Learning Course Germany
What is the Deep learning certification training/course duration?
- Total Deep learning with certification Training/ course duration will be of 32-40 hours or 5 days and it will be Instructor lead training at Mildain Training with Practical training with live project. The timing will be according to your convenience or the schedules will be mailed to the enrolled students prior to the commencement of the class, it can be on weekend and weekdays.
Mildaintrainings provide the best deep learning course. Click here and fill the form form, we will contact you back asap
Why should I take Deep learning certification course/Boot camp Training from mildaintrainings.com?
You should try Deep learning Training from Mildaintrainings as our trainers have 10 plus years of industry practical experience & we also provide Practical training with a live project so that you could understand each and everything better, it will help you in your job. At Mildaintraining we will provide you six months technical support and job support as well.
Do I get the Deep Learning Master Course Training Certificate?
- Yes at Mildaintrainings we will provide you participation certificate after the completion of Deep Learning Master Course from Mildaintraining.
What if I miss the deep learning training class(es)?
- If you miss the class in that case backup class can be adjusted in next live session.However, at Mildain Solution/Mildain training, we do not provide demo class but we have a policy that if you do not like first two (2) sessions than your whole amount will be refunded without detection.
Testing Engineer at CSC
The program was really knowledgeable and the modules were just perfectly made and managed and were taught with ease.
I am so happy I choose mildain
Senior EDI Analyst at SIQES
IBM Sterling Integrator:
Good training. Explaining the things with practical examples. Well experienced and confident enough to answer every query.
Trainers are working more as a friend rather than working like for money. Worth of paying for the course.
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Best deep learning course by Mildaintrainings. Deep Learning Training stats with deep learning introduction and a quick recap of machine learning concepts with labs assignments and hands-on practical sessions.
Enroll now for deep learning course become master of deep learning, CNN, RNN by learning deep learning master course.
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