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Many of the Syrians are very white and American-looking, very interesting. Much like the current King of Jordan

Haha! He's about 500m from the castle and it's on a massive hill behind him. She also gave him directions (In Danish!!?) telling him to go right, if he turned round it would be left....braw film though!

Syria .. before ASSad.

I'm very interested. Can I use this driver?

This is the version of the city my grandad grew up in, absolutely lovely

😊

😂

1950s and 1960s the golden age of the Middle East, the Nasser era

The Manchester docks or Salford docks is where Media City was now developed. The ship canal brought more business into Manchester ahead of Liverpool because goods were kept drier inland

Hey Thanks for the video! can i get the driver for this card

i suggest putting the special driver on a google drive that everyone can access but not modify

Wonderful, charming film.

My old Mate worked the locks at mode wheel his name was Lou Thomas and worked them for 30 years

What happened to them 😢

The smartly dressed people going about their everyday business in a relatively crime free Edinburgh is representative of most wokeless societies throughout the free nations of the Western world. Cultural diversity and drugs have changed all that.

Common mistake. It was inaugurated as the Manchester to Liverpool railway not Liverpool to Manchester.

Great job, can I get this driver?

how do you get around the licencing issues needed for these cards?

City of my birth. Fascinating to see how it looked, and how people lived and worked there, during my parents and grandparent's generation.

Another crazy closure!

A really interesting look into the past of Edinburgh, my earliest memories are from the 70's, some things still seem to be the same. One thing I did notice at 16.26 you can clearly see a young boy urinating into the street and two shocked ladies moving away from him. So Edinburgh 1948 - warts and all :)

can you provide code?

This great video is done in 1957 to be exact.

I remember going to belle vue as a little girl. Fun times.

Forgive them, for they know not what they do. Seriously, removing the railway from Otley was a massive mistake.

These cards are useless for gaming. How did You think that it can best an RTX 3090? These are the same chips as GTX 1080Ti. Guess what, my 1080Ti full hd 1080p resolution could achieve higher score than this Tesla P100 at 720p. Even my rtx 3060 can beat this benchmark score easily. So these P100s are not that impressive. Unless You could buy it dirt cheap. P100 worth about half of the GTX 1080Ti on used market. Maybe even less as You need to add another graphics card as P100 has no output, no HDMI or DP.

J'ai connu le la Syrie et le Liban des années 90, évidemment ça fait quand même une drôle d'impression après toutes les destructions que l'Occident a pu entraîner sur ce Proche-Orient qui s'était libérée du joug ottoman. La Syrie était déjà le pays le plus alphabétisé des pays du Machrek.

From, what little I know about him.He helped maintain the peace in the middle east.

A fascinating glimpse into Edinburgh unfortunately long gone…

Great quality... thanks

Edinburgh hasn't changed too much.. still recognise a lot of the streets.

Funny.... the ship "Løgstør" is danish. The Ø is a danish letter, and the name is a danish city in Jutland, Denmark. And boy does he look like a dane.....I love love love Edinburgh, been there a ton of times...... and I think I might have a wee lassie waiting for me there....

Where can get this driver. Or how to enable the functions?

75 years ago all people could talk about politics and speak good English , it only took 65years to destroy them . Very sad Long life Syria 🙏♥️

No country was well after the second world war . Neither the UK nor Europe ! The UK erstwhile showed excellent railways systems,mainly in Scotland ! Very interesting to have watched on Edinburgh about 1948 . . . Thanks a plenty !

Where did u get the vid I want to find the source code and I cant find anything

64 cores wow! is it dual socket or quad socket server?

Can this driver also enable everything for P40?

Very enjoyable and interesting but spoilt by poor audio drowned out by music in places.

could you explain what is volume actually means in a particular time or any other, like a trader bought 10 shares + another trader sold 10 shares to the buyer = 20 shares traded is that volume? please explain

why do majority people use price lagging indicators, they don't give profits in the short term trading, could you use price action trading so that majority wants to see price action trading as the main trading tool. Please use price action alone for trading using your machine learning that will be the game changer.

I see many stock market analysis, majority use moving averages, why they won't talk much or use quantitative analysis using price action top down approach, Elliott wave theory or Wycoff ?, could you use these price action trading on machine learning models, in combination of your technical indicators?

Syria 👍

Absolutely brilliant to see Edinburgh back then.

Hello I just got my hands on one. Can I get the drivers?

Thank you for your teaching! 0:01:31 3 mini courses 1. Manipulating Financial Data in Python 2. Computational Investing 3. Learning Algorithms for Trading 0:02:33 Textbooks 1. Python for Finance 2. What Hedge Funds Really Do 3. Machine Learning 0:04:24 Python Features 0:05:23 Comma Separated Values files with headers 0:12:21 Pandas dataframe 0:15:33 df = pd.read_csv('directory_path/file_name.csv') 0:16:35 print('The top 5 lines of XXX data ', df.tail()) 0:17:00 index column added by Pandas dataframe to access rows 0:17:20 Slicing to get the data for a range by index print('The range from the 11th line to 20th line of 10 lines for XXX data ', df[11:20 + 1]) print(df[33:99 + 1][['Date','High']]) df['Date'] = pd.to_datetime(df['Date']) df_october = df[(df['Date'].dt.year == 2020) & (df['Date'].dt.month == 10)] start_date = pd.to_datetime("2020-10-01") end_date = pd.to_datetime("2020-10-31") df_filtered = df[df["Date"].between(start_date, end_date)] 0:18:06 max(), mean(), min() methods in Pandas dataframe df[('Close')}.max() print('The maximum value of the "Close" column for XXX data ', df['Close'].max()) 0:30:47 Build a dataframe in Pandas with a time range start_date = '2020-01-01' end_date = '2020-01-31' df_dates = pd.date_range(start_date, end_date) 0:34:43 df = pd.read_csv('data.csv', index_col = 'Date', parse_dates = True, usecols = ['Date', 'Close']) df = df.set_index("Date") 0:36:36 df = df.dropna() 0:46:26 df = df.set_index("Date") print(df["2020-10-01":"2020-10-02"][['Open','High']]) 0:50:30 Plotting plt_df = df.plot(title = 'title', fontsize = 12) plt_df.set_xlabel('Data') plt_df.set_ylabel('Price') plt.show() 0:53:05 Normalize data df = df / df[0,:] df = df.div(df.iloc[:, 0], axis=0) 0:55:45 Slicing on ndarrays for Numpy nda[0:3, -5:-2] 1:02:30 Creating ndarrays np.empty(5) np.empty((5, 3)) print(np.ones((2, 3, 4, 5))) 1:06:01 Generating random numbers print(np.random.random((3, 4))) # between 0 to less than 1 print(np.random.randint(10)) # a integer in 0 to 9 print(np.random.randint(6, 10)) # a integer by a range 6 to less than 10 print(np.random.randint(10, 110, size = 5)) # 5 intergers in a array print(np.random.randint(1, 11, size = (2, 3, 4, 5))) 1:09:31 Checking the rows and columns of ndarrays print(df.shape) print(df.shape[0]) print(df.shape[1]) 1:10:16 Checking the dimension of ndarrays print(len(df.shape)) 1:10:35 Counting total number for elements of ndarrays print(df.size) print(df.dtype) 1:12:18 Calculation for ndarrays print('Sum of all elements: ', df.sum()) print('Sum of each column: ', df.sum(axis = 0)) print('Sum of each row: ', df.sum(axis = 1)) 1:14:03 print('Maximum of each column: ', df.max(axis = 0)) print('Minimum of each row: ', df.min(axis = 1)) print('Mean of all elements: ', df.mean()) 1:15:30 Checking the index of a specific condition print('The index of the maximum value: ', df.argmax()) 1:19:05 Accessing for ndarrays 1:20:55 Slicing non-series columns df[:, 0:3:2] 1:21:43 Assigning ndarray elements df[0, :] = [1, 2, 3, 4] 1:23:08 Assigning ndarray elements with other ndarrays nda1[1, 1, 2, 4] nda2[nad1] # the values of the indices of nda1 are assigned to nda2 array 1:24:40 Boolean ndarrays 1:25:40 Masking mean_df = df.mean() print(df[df < mean_df]) df[df < mean_df] = mean_df 1:26:27 Arithmetic operations by elements' indeices 1:27:05 Divisions give the results with C language attribute, not Python's 1:30:13 Global statistics 1:31:28 df.mean() df.median() df.std() 1:33:21 Rolling statistics 1:37:09 Bollinger bands 1:44:39 Daily returns 1:50:24 Shift column diff_df = (df[1:] / df[:-1].values) - 1 diff_df = (df / df.shift(1)) - 1 diff_df.iloc[0, :] = 0 1:50:47 Cumulative returns cum_df = (df.iloc[-1] / df.iloc[0]) - 1 diff_df = (df[:, -1] / df[:, 0].fillna(0)) - 1 1:53:09 Historical financial data 2:02:53 Pandas fillna() df.fillna(0, inplace = True) df.ffill(inplace = True) df.bfill(inplace = True) 2:12:40 Plot a histogram 2:25:00 Scatter plots 2:31:20 Daily portfolio value 2:35:57 Portfolio statistics 2:40:21 Sharpe ratio risk adjusted return 2:52:30 Optimizer 3:02:40 Convex problems 3:05:49 Building a parameterized model 3:24:52 Framing the problem provide a function to minimize provide an initiial guess for x call the optimizer 3:26:53 Ranges and constraints Ranges: limits on values for x Constrains: properties of x that must be true 3:29:38 Types of funds ETF Buy/Sell like stocks Baskets of stocks Transparent Liquid Mutual fund Buy/Sell at end of day Quarterly disclosure Less transparent Large cap Hedge fund Buy/Sell by agreement No disclosure Not transparent 3:37:16 Incentives: How are they compensated? 4:01:13 What is in an order? 4:03:58 The order book 4:11:34 How orders get to the exchange 4:15:40 How hedge funds exploit market mechanics 4:19:48 Additional order types 4:29:35 Why company value matters 4:35:51 The value of a future dollar 4:45:25 What is the value? intrinsic value 4:45:58 Book value Total assets minus intangible assets and liabilities 4:48:05 Market cappitalization number of shares x price 4:57:09 Definition of a portfolio 5:04:09 The CAPM equation 5:17:08 Arbitrage pricing theory 5:33:03 Characteristics of Technical Analysis 5:55:02 How data is aggregated 6:00:43 Stock splits 6:07:04 Dividends 6:14:15 Survivor bias 6:17:07 EMH assumption 6:21:02 3 forms of the EMH Weak : Future prices cannot be predicted by analyzing historical prices Semi-strong: Prices adjust rapidly to new public information Strong : Prices reflect all information public and private 6:28:36 Grinold's Fundamental Law Performance Skill Breadth 6:29:24 Performance = Skill * Breadth**(1/2) '''Skill much dominant than Breadth so/but breadth has to be at least 1 not to diminish the total, performance''' 6:44:16 Real World RenTec trades 100k/day (some say 2k/0.3sec) BerkHath holds 120 stocks 6:48:55 IR =IC * BR**(1/2) Information Ratio Information Coefficient(correlation of forecasts to returns) BReadth number of trading opportunities per year 6:53:00 What is risk? 6:58:53 The importance of covariance 7:02:26 Mean Variance Optimization Inputs: Expected return Volatilaty Covariance Target return Output: Asset weights for portfolio that minimize risk

7:08:44 Machine Learning 7:09:13 The Machine Learning problem 7:10:00 on Model in ML observation multi-demensional prediction sigle dimension 7:11:42 Supervised regression learning regression: numerical prediction supervised: provide example x, y learning: train with data 7:13:00 Linear regression(parametric) k nearest neighbor(KNN)(instance based) decision trees decision forests 7:14:50 Example Training Episode Robot car 7:17:52 How it works with stock data 7:26:00 Backtesting 7:29:40 Problems with regression noisy and uncertain challenging to estimate confidence holding time, allocation 7:31:19 Policy Learning RL 7:33:25 Parametric regression 7:37:37 K nearest neighbor(KNN) 7:40:49 Parametric or non-parametric 7:45:42 Training and testing 7:48:31 Learning APIs For Linear regression: learner = LinRegLearner() learner.train(Xtrain, Ytrain) y = learner.query(Xtest) For KNN: learner = KNNLearner(K = 3) learner.train(Xtrain, Ytrain) y = learner.query(Xtest) 7:49:46 Example for linear regression class LinRegLearner(): def __init__(): pass def train(x, y): self.m, self.b = xxx-linreg(x, y) def qery(x): y = self.m * X + self.b return y 7:52:23 A closer look at KNN solutions 7:59:14 Metric1: RMS error ((sum * ((Ytest - Ypredict) ** 2)) / N ** (1/2)) 8:02:11 Cross validation for train - 60% data for test - 40% data 80:20 8:04:07 Metric2: Correlation 8:07:13 Overfitting The train and test results diverge 8:11:17 A few other considerations 8:13:53 Ensemble learners Taking the mean of the results for the multiple types of models 8:16:45 How to build an ensemble? 8:18:19 Bootstrap aggregating - bagging n number of instances n' number in a bag random with replacement m number of bags different models 8:23:32 Boosting: Ada Boost an ensemble learning algorithm that combines weak learners to make a strong learner 8:26:39 Boosting and bagging wrappers for existing methods reduces error reduces overfitage 8:27:41 Reinforcement Learning 8:28:10 The Reinforcement Learning problem state policy action reward 8:32:05 Q: Trading as an RL problem 8:34:00 Mapping trading to RL 8:35:51 Markov decision problems Set of states S Set of actions A Transition function T[s, a, s'] Reward function R[s, a] Find policy ㅍ*(s) that wil maximize reward * express 'optimum' 8:38:14 Unknown transitions and rewards Model-based Build model of T[s, a, s'] R[s, a] Value/Policy iteration Model-free Q-Learning 8:41:09 What to optimize? infinite horizon finite horizon discounted reward - for Q-Learning 8:47:41 Q: Which gets $1M? 8:49:14 RL summary RL algos solve MDPs S, A, T[s, a, s'], R[s, a] Find ㅍ(s) -> a Map trading to RL 8:51:03 Q-Learning - model-free approches 8:51:42 What is Q? - table, not greedy Q[s, a] = immediate reward + discounted reward(for future actions) How to use Q? ㅍ(s) = argmax`a(Q[s, a]) ㅍ*(s) Q*[s, a] 8:54:34 Q Learning procedure Big picture select training data iterate over time <s, a, s', r> test policy ㅍ repeat until converge Details set starttime, init Q[] compute s select a observer r, s' <s, a, s', r> update Q 8:57:57 Update rule alpha learning rate 0 to 1 (0.2) -larger faster gamma discount rage 0 t0 1 -lower lower later rewards by high discount rate Q'[s, a] = (1 - alpha) * Q[s, a] + alpha * improved estimate Q'[s,a]=(1-alpha)Q[s,a]+alpha*improved estimate Q'[s, a] = (1 - alpha) * Q[s, a] + alpha * (r + gamma * later rewards) Q'[s, a] = (1 - alpha) * Q[s, a] + alpha * (r + gamma * Q[s', argmax`a'(Q[s', a'])]) Q'[s,a]=(1-alpha)Q[s,a]+alpha(r+gammaQ[s',argmax`a'(Q[s', a,])]) 9:03:04 Two finer points Sucess depends on exploration Choose random action with prob c 9:04:35 The trading problem: Actions Buy Sell Nothing 9:07:58 Q: The trading problem: Rewards 9:08:29 The trading problem: State 9:10:31 Creating the state state is an integer discretize each factor combine 9:12:23 Discretizing stepsize = size(data) / steps data.sort() for i in range(0, steps) threshold[i] = data[(i + 1) * stepsize)] 9:14:17 Q-Learning Recap Building a model define states, actions, rewards choose in-sample training period iterate: Q-table update backtest Testing a model backtest on later data 9:15:54 Dyna-Q Big Picture Q-Learn init Q table observe s execute a, observe s', r update Q with <s, a, s', r> repeat => expensive Dyna-Q Learn model T(state transition function) R(reward function) Hallucinate experience Update Q repeat 100 - 200 => cheap T'[s, a, s'] R'[s, a] update each model s = random a = random s' = infer from T[ } r = R[s, a] update Q w/ <s, a, s', r> 9:20:08 Learning T T[s, a, s'] prob s, a -> s' init Tc[ ] = 0.00001 c: count while executing, observe s, a, s' increment Tc[s, a, s'] 9:21:43 Q: How to evaluate T? T[s, a, s'] = Tc[s, a, s'] / sum of i * T[s, a, i] 9:23:23 Learning R R[s, a] expected reward for s, a r immadiate reward R'[s, a] = (1 - alpha) * R[s, a] + alpha * gamma 9:25:03 Dyna-Q recap Q-Learn init Q table observe s execute a, observe s', r update Q with <s, a, s', r> repeat => expensive Dyna-Q T'[s, a, s'] R'[s, a] update each model s = random a = random s' = infer from T[ } r = R[s, a] update Q w/ <s, a, s', r> 9:25:59 Interview with Quandl founder Nov 4, 2023 Mon 14:32 PST

안녕하세요. 감사히 공부했어요. Thank you for your teaching! I'm learning your 'Machine Learning for Trading' also. Nov 29, 2023 Wed 22:16 PST

This is not algerian flag.... This is the flag of Algeria.. 🇩🇿

Trying to achieve something similar with a Tesla P4. Would be very interested to know how you unlocked all the features Thanks !